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2019-09-23
Zheng, N., Alawini, A., Ives, Z. G..  2019.  Fine-Grained Provenance for Matching ETL. 2019 IEEE 35th International Conference on Data Engineering (ICDE). :184–195.
Data provenance tools capture the steps used to produce analyses. However, scientists must choose among workflow provenance systems, which allow arbitrary code but only track provenance at the granularity of files; provenance APIs, which provide tuple-level provenance, but incur overhead in all computations; and database provenance tools, which track tuple-level provenance through relational operators and support optimization, but support a limited subset of data science tasks. None of these solutions are well suited for tracing errors introduced during common ETL, record alignment, and matching tasks - for data types such as strings, images, etc. Scientists need new capabilities to identify the sources of errors, find why different code versions produce different results, and identify which parameter values affect output. We propose PROVision, a provenance-driven troubleshooting tool that supports ETL and matching computations and traces extraction of content within data objects. PROVision extends database-style provenance techniques to capture equivalences, support optimizations, and enable selective evaluation. We formalize our extensions, implement them in the PROVision system, and validate their effectiveness and scalability for common ETL and matching tasks.
2019-09-09
Rathi, P. S., Rao, C. M..  2018.  An Enhanced Threshold Based Cryptography with Secrete Sharing and Particle Swarm Optimization for Data Sending in MANET. 2018 3rd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS). :87-91.

There are two types of network architectures are presents those are wired network and wireless network. MANETs is one of the examples of wireless network. Each and every network has their own features which make them different from other types of network. Some of the features of MANETs are; infrastructure less network, mobility, dynamic network topology which make it different and more popular from wired network but these features also generate different problems for achieving security due to the absence of centralized authority inside network as well as sending of data due to its mobility features. Achieving security in wired network is little-bit easy compare to MANETs because in wired network user need to just protect main centralized authority for achieving security whereas in MANETs there is no centralized authority available so protecting server in MANETs is difficult compare to wired network. Data sending and receiving process is also easy in wired network but mobility features makes this data sending and receiving process difficult in MANETs. Protecting server or central repository without making use of secrete sharing in wired network will create so many challenges and problem in terms of security. The proposed system makes use of Secrete sharing method to protect server from malicious nodes and `A New particle Swarm Optimization Method for MANETs' (NPSOM) for performing data sending and receiving operation in optimization way. NPSOM technique get equated with the steady particle swarm optimizer (PSO) technique. PSO was essentially designed by Kennedy, Eberhart in 1995. These methods are based upon 4 dissimilar types of parameters. These techniques were encouraged by common performance of animals, some of them are bird assembling and fish tuition, ant colony. The proposed system converts this PSO in the form of MANETs where Particle is nothing but the nodes in the network, Swarm means collection of multiple nodes and Optimization means finding the best and nearer root to reach to destination. Each and every element study about their own previous best solution which they are having with them for the given optimization problem, likewise they see for the groups previous best solution which they got for the same problem and finally they correct its solution depending on these values. This same process gets repeated for finding of the best and optimal solutions value. NPSOM technique, used in proposed system there every element changes its location according to the solution which they got previously and which is poorest as well as their collection's earlier poorest solution for finding best, optimal value. In this proposed system we are concentrating on, sidestepping element's and collections poorest solution which they got before.

2019-08-26
Chiu, Pei-Ling, Lee, Kai-Hui.  2018.  Optimization Based Adaptive Tagged Visual Cryptography. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :33–34.
The Tagged Visual Cryptography Scheme (TVCS)1 adds tag images to the noise-like shares generated by the traditional VCS to improve the shares management of the traditional VCS. However, the existing TVCSs suffers visual quality of the recovered secret image may be degraded and there may be pixel expansion. This study proposes a Threshold Adaptive Tagged Visual Cryptography Scheme ((k, n)-ATVCS) to solve the above-mentioned problems. The ATVCS encryption problem is formulated in a mathematical optimization model, and an evolutionary algorithm is developed to find the optimal solution to the problem. The proposed (k, n)-ATVCS enables the encryptor to adjust the visual quality between the tag image and the secret image by tuning parameters. Experimental results show the correctness and effectiveness of this study.
2019-06-28
Chen, G., Wang, D., Li, T., Zhang, C., Gu, M., Sun, J..  2018.  Scalable Verification Framework for C Program. 2018 25th Asia-Pacific Software Engineering Conference (APSEC). :129-138.

Software verification has been well applied in safety critical areas and has shown the ability to provide better quality assurance for modern software. However, as lines of code and complexity of software systems increase, the scalability of verification becomes a challenge. In this paper, we present an automatic software verification framework TSV to address the scalability issues: (i) the extended structural abstraction and property-guided program slicing to solve large-scale program verification problem, saving time and memory without losing accuracy; (ii) automatically select different verification methods according to the program and property context to improve the verification efficiency. For evaluation, we compare TSV's different configurations with existing C program verifiers based on open benchmarks. We found that TSV with auto-selection performs better than with bounded model checking only or with extended structural abstraction only. Compared to existing tools such as CMBC and CPAChecker, it acquires 10%-20% improvement of accuracy and 50%-90% improvement of memory consumption.

2019-06-24
Chouikhi, S., Merghem-Boulahia, L., Esseghir, M..  2018.  Energy Demand Scheduling Based on Game Theory for Microgrids. 2018 IEEE International Conference on Communications (ICC). :1–6.

The advent of smart grids offers us the opportunity to better manage the electricity grids. One of the most interesting challenges in the modern grids is the consumer demand management. Indeed, the development in Information and Communication Technologies (ICTs) encourages the development of demand-side management systems. In this paper, we propose a distributed energy demand scheduling approach that uses minimal interactions between consumers to optimize the energy demand. We formulate the consumption scheduling as a constrained optimization problem and use game theory to solve this problem. On one hand, the proposed approach aims to reduce the total energy cost of a building's consumers. This imposes the cooperation between all the consumers to achieve the collective goal. On the other hand, the privacy of each user must be protected, which means that our distributed approach must operate with a minimal information exchange. The performance evaluation shows that the proposed approach reduces the total energy cost, each consumer's individual cost, as well as the peak to average ratio.

2019-03-25
Yu, Kuai, Gu, Naijie, Su, Junjie, Bai, Qilin.  2018.  Efficient Software Implementation of ZUC Stream Cipher. Proceedings of the 2Nd International Conference on Vision, Image and Signal Processing. :52:1–52:6.
ZUC stream cipher is the first stream cipher developed independently by Chinese cryptologists as an international standard. The fast implementation of encryption algorithm is an important issue in cryptography application. At present, the research on ZUC stream cipher is mainly based on hardware implementation, and there are many efficient hardware implementations of ZUC stream cipher, but there are few efficient software implementations at present. This paper presents an efficient software design and implementation of ZUC stream cipher. Firstly, we propose the delayed modular, sliding window, and S-box optimizations to reduce the computational cost without modifying the calculation result of ZUC stream cipher. Secondly, single instruction multiple data instructions, reducing the times of memory access, loop unrolling optimization and other code optimization methods can improve the speed of encryption and decryption. Finally, we design and implementation a genetic algorithm to find the optimal sequence of optimizations in compiler. Experiments show that compared with the implementation of ZUC stream cipher given in the official document, these methods can give 102% performance improvement.
Ali-Tolppa, J., Kocsis, S., Schultz, B., Bodrog, L., Kajo, M..  2018.  SELF-HEALING AND RESILIENCE IN FUTURE 5G COGNITIVE AUTONOMOUS NETWORKS. 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K). :1–8.
In the Self-Organizing Networks (SON) concept, self-healing functions are used to detect, diagnose and correct degraded states in the managed network functions or other resources. Such methods are increasingly important in future network deployments, since ultra-high reliability is one of the key requirements for the future 5G mobile networks, e.g. in critical machine-type communication. In this paper, we discuss the considerations for improving the resiliency of future cognitive autonomous mobile networks. In particular, we present an automated anomaly detection and diagnosis function for SON self-healing based on multi-dimensional statistical methods, case-based reasoning and active learning techniques. Insights from both the human expert and sophisticated machine learning methods are combined in an iterative way. Additionally, we present how a more holistic view on mobile network self-healing can improve its performance.
2019-03-11
Li, Z., Xie, X., Ma, X., Guan, Z..  2018.  Trustworthiness Optimization of Industrial Cluster Network Platform Based on Blockchain. 2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS). :1–6.

Industrial cluster is an important organization form and carrier of development of small and medium-sized enterprises, and information service platform is an important facility of industrial cluster. Improving the credibility of the network platform is conducive to eliminate the adverse effects of distrust and information asymmetry on industrial clusters. The decentralization, transparency, openness, and intangibility of block chain technology make it an inevitable choice for trustworthiness optimization of industrial cluster network platform. This paper first studied on trusted standard of industry cluster network platform and construct a new trusted framework of industry cluster network platform. Then the paper focus on trustworthiness optimization of data layer and application layer of the platform. The purpose of this paper is to build an industrial cluster network platform with data access, information trustworthiness, function availability, high-speed and low consumption, and promote the sustainable and efficient development of industrial cluster.

2019-03-06
Li, W., Li, S., Zhang, X., Pan, Q..  2018.  Optimization Algorithm Research of Logistics Distribution Path Based on the Deep Belief Network. 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). :60-63.

Aiming at the phenomenon that the urban traffic is complex at present, the optimization algorithm of the traditional logistic distribution path isn't sensitive to the change of road condition without strong application in the actual logistics distribution, the optimization algorithm research of logistics distribution path based on the deep belief network is raised. Firstly, build the traffic forecast model based on the deep belief network, complete the model training and conduct the verification by learning lots of traffic data. On such basis, combine the predicated road condition with the traffic network to build the time-share traffic network, amend the access set and the pheromone variable of ant algorithm in accordance with the time-share traffic network, and raise the optimization algorithm of logistics distribution path based on the traffic forecasting. Finally, verify the superiority and application value of the algorithm in the actual distribution through the optimization algorithm contrast test with other logistics distribution paths.

Lin, Y., Liu, H., Xie, G., Zhang, Y..  2018.  Time Series Forecasting by Evolving Deep Belief Network with Negative Correlation Search. 2018 Chinese Automation Congress (CAC). :3839-3843.

The recently developed deep belief network (DBN) has been shown to be an effective methodology for solving time series forecasting problems. However, the performance of DBN is seriously depended on the reasonable setting of hyperparameters. At present, random search, grid search and Bayesian optimization are the most common methods of hyperparameters optimization. As an alternative, a state-of-the-art derivative-free optimizer-negative correlation search (NCS) is adopted in this paper to decide the sizes of DBN and learning rates during the training processes. A comparative analysis is performed between the proposed method and other popular techniques in the time series forecasting experiment based on two types of time series datasets. Experiment results statistically affirm the efficiency of the proposed model to obtain better prediction results compared with conventional neural network models.

2019-02-21
Gao, Y..  2018.  An Improved Hybrid Group Intelligent Algorithm Based on Artificial Bee Colony and Particle Swarm Optimization. 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). :160–163.
Aiming at the disadvantage of poor convergence performance of PSO and artificial swarm algorithm, an improved hybrid algorithm is proposed to overcome the shortcomings of complex optimization problems. Through the test of four standard function by hybrid algorithm and compared the result with standard particle swarm optimization (PSO) algorithm and Artificial Bee Colony (ABC) algorithm, the convergence rate and convergence precision of the hybrid algorithm are both superior to those of the standard particle swarm algorithm and Artificial Bee Colony algorithm, presenting a better optimal performance.
Xie, S., Wang, G..  2018.  Optimization of parallel turnings using particle swarm intelligence. 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI). :230–234.
Machining process parameters optimization is of concern in machining fields considering machining cost factor. In order to solve the optimization problem of machining process parameters in parallel turning operations, which aims to reduce the machining cost, two PSO-based optimization approaches are proposed in this paper. According to the divide-and-conquer idea, the problem is divided into some similar sub-problems. A particle swarm optimization then is derived to conquer each sub-problem to find the optimal results. Simulations show that, comparing to other optimization approaches proposed previously, the proposed two PSO-based approaches can get optimal machining parameters to reduce both the machining cost (UC) and the computation time.
2019-01-31
Chen, Y., Wu, B..  2018.  An Efficient Algorithm for Minimal Edit Cost of Graph Degree Anonymity. 2018 IEEE International Conference on Applied System Invention (ICASI). :574–577.

Personal privacy is an important issue when publishing social network data. An attacker may have information to reidentify private data. So, many researchers developed anonymization techniques, such as k-anonymity, k-isomorphism, l-diversity, etc. In this paper, we focus on graph k-degree anonymity by editing edges. Our method is divided into two steps. First, we propose an efficient algorithm to find a new degree sequence with theoretically minimal edit cost. Second, we insert and delete edges based on the new degree sequence to achieve k-degree anonymity.

2019-01-21
Feng, S., Xiong, Z., Niyato, D., Wang, P., Leshem, A..  2018.  Evolving Risk Management Against Advanced Persistent Threats in Fog Computing. 2018 IEEE 7th International Conference on Cloud Networking (CloudNet). :1–6.
With the capability of support mobile computing demand with small delay, fog computing has gained tremendous popularity. Nevertheless, its highly virtualized environment is vulnerable to cyber attacks such as emerging Advanced Persistent Threats attack. In this paper, we propose a novel approach of cyber risk management for the fog computing platform. Particularly, we adopt the cyber-insurance as a tool for neutralizing cyber risks from fog computing platform. We consider a fog computing platform containing a group of fog nodes. The platform is composed of three main entities, i.e., the fog computing provider, attacker, and cyber-insurer. The fog computing provider dynamically optimizes the allocation of its defense computing resources to improve the security of the fog computing platform. Meanwhile, the attacker dynamically adjusts the allocation of its attack resources to improve the probability of successful attack. Additionally, to prevent from the potential loss due to attacks, the provider also makes a dynamic decision on the purchases ratio of cyber-insurance from the cyber-insurer for each fog node. Thereafter, the cyber-insurer accordingly determines the premium of cyber-insurance for each fog node. In our formulated dynamic Stackelberg game, the attacker and provider act as the followers, and the cyber-insurer acts as the leader. In the lower level, we formulate an evolutionary subgame to analyze the provider's defense and cyber-insurance subscription strategies as well as the attacker's attack strategy. In the upper level, the cyber-insurer optimizes its premium determination strategy, taking into account the evolutionary equilibrium at the lower-level evolutionary subgame. We analytically prove that the evolutionary equilibrium is unique and stable. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the dynamic Stackelberg game.
Kafash, S. H., Giraldo, J., Murguia, C., Cárdenas, A. A., Ruths, J..  2018.  Constraining Attacker Capabilities Through Actuator Saturation. 2018 Annual American Control Conference (ACC). :986–991.
For LTI control systems, we provide mathematical tools - in terms of Linear Matrix Inequalities - for computing outer ellipsoidal bounds on the reachable sets that attacks can induce in the system when they are subject to the physical limits of the actuators. Next, for a given set of dangerous states, states that (if reached) compromise the integrity or safe operation of the system, we provide tools for designing new artificial limits on the actuators (smaller than their physical bounds) such that the new ellipsoidal bounds (and thus the new reachable sets) are as large as possible (in terms of volume) while guaranteeing that the dangerous states are not reachable. This guarantees that the new bounds cut as little as possible from the original reachable set to minimize the loss of system performance. Computer simulations using a platoon of vehicles are presented to illustrate the performance of our tools.
2018-12-10
Murray, B., Islam, M. A., Pinar, A. J., Havens, T. C., Anderson, D. T., Scott, G..  2018.  Explainable AI for Understanding Decisions and Data-Driven Optimization of the Choquet Integral. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.
To date, numerous ways have been created to learn a fusion solution from data. However, a gap exists in terms of understanding the quality of what was learned and how trustworthy the fusion is for future-i.e., new-data. In part, the current paper is driven by the demand for so-called explainable AI (XAI). Herein, we discuss methods for XAI of the Choquet integral (ChI), a parametric nonlinear aggregation function. Specifically, we review existing indices, and we introduce new data-centric XAI tools. These various XAI-ChI methods are explored in the context of fusing a set of heterogeneous deep convolutional neural networks for remote sensing.
2018-11-19
Yang, M., Wang, A., Sun, G., Liang, S., Zhang, J., Wang, F..  2017.  Signal Distribution Optimization for Cabin Visible Light Communications by Using Weighted Search Bat Algorithm. 2017 3rd IEEE International Conference on Computer and Communications (ICCC). :1025–1030.
With increasing demand for travelling, high-quality network service is important to people in vehicle cabins. Visible light communication (VLC) system is more appropriate than wireless local area network considering the security, communication speed, and narrow shape of the cabin. However, VLC exhibits technical limitations, such as uneven distribution of optical signals. In this regard, we propose a novel weight search bat algorithm (WSBA) to calculate a set of optimal power adjustment factors to reduce fluctuation in signal distributions. Simulation results show that the fairness of signal distribution in the cabin optimized by WSBA is better than that of the non-optimized signal distribution. Moreover, the coverage rate of WSBA is higher than that of genetic algorithm and particle swarm optimization.
Gupta, A., Johnson, J., Alahi, A., Fei-Fei, L..  2017.  Characterizing and Improving Stability in Neural Style Transfer. 2017 IEEE International Conference on Computer Vision (ICCV). :4087–4096.
Recent progress in style transfer on images has focused on improving the quality of stylized images and speed of methods. However, real-time methods are highly unstable resulting in visible flickering when applied to videos. In this work we characterize the instability of these methods by examining the solution set of the style transfer objective. We show that the trace of the Gram matrix representing style is inversely related to the stability of the method. Then, we present a recurrent convolutional network for real-time video style transfer which incorporates a temporal consistency loss and overcomes the instability of prior methods. Our networks can be applied at any resolution, do not require optical flow at test time, and produce high quality, temporally consistent stylized videos in real-time.
Huang, X., Belongie, S..  2017.  Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization. 2017 IEEE International Conference on Computer Vision (ICCV). :1510–1519.
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.
Wang, X., Oxholm, G., Zhang, D., Wang, Y..  2017.  Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). :7178–7186.
Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly real-time by performing much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.
Grinstein, E., Duong, N. Q. K., Ozerov, A., Pérez, P..  2018.  Audio Style Transfer. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :586–590.
``Style transfer'' among images has recently emerged as a very active research topic, fuelled by the power of convolution neural networks (CNNs), and has become fast a very popular technology in social media. This paper investigates the analogous problem in the audio domain: How to transfer the style of a reference audio signal to a target audio content? We propose a flexible framework for the task, which uses a sound texture model to extract statistics characterizing the reference audio style, followed by an optimization-based audio texture synthesis to modify the target content. In contrast to mainstream optimization-based visual transfer method, the proposed process is initialized by the target content instead of random noise and the optimized loss is only about texture, not structure. These differences proved key for audio style transfer in our experiments. In order to extract features of interest, we investigate different architectures, whether pre-trained on other tasks, as done in image style transfer, or engineered based on the human auditory system. Experimental results on different types of audio signal confirm the potential of the proposed approach.
Huang, H., Wang, H., Luo, W., Ma, L., Jiang, W., Zhu, X., Li, Z., Liu, W..  2017.  Real-Time Neural Style Transfer for Videos. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). :7044–7052.
Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images. In this work, we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain temporal consistency among stylized video frames. Our feed-forward network is trained by enforcing the outputs of consecutive frames to be both well stylized and temporally consistent. More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames. To calculate the temporal loss during the training stage, a novel two-frame synergic training mechanism is proposed. Compared with directly applying an existing image style transfer method to videos, our proposed method employs the trained network to yield temporally consistent stylized videos which are much more visually pleasant. In contrast to the prior video style transfer method which relies on time-consuming optimization on the fly, our method runs in real time while generating competitive visual results.
Chen, Y., Lai, Y., Liu, Y..  2017.  Transforming Photos to Comics Using Convolutional Neural Networks. 2017 IEEE International Conference on Image Processing (ICIP). :2010–2014.
In this paper, inspired by Gatys's recent work, we propose a novel approach that transforms photos to comics using deep convolutional neural networks (CNNs). While Gatys's method that uses a pre-trained VGG network generally works well for transferring artistic styles such as painting from a style image to a content image, for more minimalist styles such as comics, the method often fails to produce satisfactory results. To address this, we further introduce a dedicated comic style CNN, which is trained for classifying comic images and photos. This new network is effective in capturing various comic styles and thus helps to produce better comic stylization results. Even with a grayscale style image, Gatys's method can still produce colored output, which is not desirable for comics. We develop a modified optimization framework such that a grayscale image is guaranteed to be synthesized. To avoid converging to poor local minima, we further initialize the output image using grayscale version of the content image. Various examples show that our method synthesizes better comic images than the state-of-the-art method.
Li, P., Zhao, L., Xu, D., Lu, D..  2018.  Incorporating Multiscale Contextual Loss for Image Style Transfer. 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). :241–245.
In this paper, we propose to impose a multiscale contextual loss for image style transfer based on Convolutional Neural Networks (CNN). In the traditional optimization framework, a new stylized image is synthesized by constraining the high-level CNN features similar to a content image and the lower-level CNN features similar to a style image, which, however, appears to lost many details of the content image, presenting unpleasing and inconsistent distortions or artifacts. The proposed multiscale contextual loss, named Haar loss, is responsible for preserving the lost details by dint of matching the features derived from the content image and the synthesized image via wavelet transform. It endows the synthesized image with the characteristic to better retain the semantic information of the content image. More specifically, the unpleasant distortions can be effectively alleviated while the style can be well preserved. In the experiments, we show the visually more consistent and simultaneously well-stylized images generated by incorporating the multiscale contextual loss.
2018-09-28
Yang, Y., Wunsch, D., Yin, Y..  2017.  Hamiltonian-driven adaptive dynamic programming for nonlinear discrete-time dynamic systems. 2017 International Joint Conference on Neural Networks (IJCNN). :1339–1346.

In this paper, based on the Hamiltonian, an alternative interpretation about the iterative adaptive dynamic programming (ADP) approach from the perspective of optimization is developed for discrete time nonlinear dynamic systems. The role of the Hamiltonian in iterative ADP is explained. The resulting Hamiltonian driven ADP is able to evaluate the performance with respect to arbitrary admissible policies, compare two different admissible policies and further improve the given admissible policy. The convergence of the Hamiltonian ADP to the optimal policy is proven. Implementation of the Hamiltonian-driven ADP by neural networks is discussed based on the assumption that each iterative policy and value function can be updated exactly. Finally, a simulation is conducted to verify the effectiveness of the presented Hamiltonian-driven ADP.