<![CDATA[ IEEE Transactions on Systems, Man, and Cybernetics: Systems - new TOC ]]>
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TOC Alert for Publication# 6221021 2018August 16<![CDATA[Table of contents]]>489C1C1444<![CDATA[IEEE Transactions on Systems, Man, and Cybernetics publication information]]>489C2C2115<![CDATA[Blockchain and Cryptocurrencies: Model, Techniques, and Applications]]>48914211428906<![CDATA[A Gait Recognition Method for Human Following in Service Robots]]>${F} _{text {comb}}$ indexes on our dataset. Compared with five state-of-the-art gait recognition methods, the proposed method achieves the best performance on human gait recognition based on the walking sequences defined in our proposed dataset.]]>489142914401474<![CDATA[An Information Theory-Based Feature Selection Framework for Big Data Under Apache Spark]]>489144114531071<![CDATA[Evaluation of Disaster Response System Using Agent-Based Model With Geospatial and Medical Details]]>489145414693222<![CDATA[Model Learning for Multistep Backward Prediction in Dyna-<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> Learning]]>${Q}$ functions is proposed. The environment is approximated by a virtual model that can predict the transition to the next state and the reward of the domain. This virtual model is used to train ${Q}$ functions to accelerate policy learning. Lookup table methods are usually used to establish such environmental models, but these methods need to collect tremendous amounts of experiences to enumerate responses of the environment. In this paper, a stochastic model learning method based on tree structures is presented. To model the transition probability, an online clustering method is applied to equip the model learning method with the abilities to evaluate the transition probability. By the virtual model, the RL method produces simulated experience in the stage of indirect learning. Since simulated transitions and backups are more usefully focused by working backward from the state-action, the pair estimated ${Q}$ value of which changes significantly, the useful one-step backups are actions that lead directly into the one state whose value has already obviously been changed. This, however, may induce a false positive; that is, a backup state may be an invalid state, such as an absorbing or terminal state, especially in cases where the changes of ${Q}$ values at the planning stage are still needed to put back for ranking even though they are based on a simulated experience and are possibly erroneous. It is obvious that when the agent is attracted to generate simulated experience around the area of these absorbing states, the learning efficiency is deteriorated. This -
aper proposes three detecting methods to solve this problem. Moreover, the policy learning can speed up. The effectiveness and generality of our method is further demonstrated in three numerical simulations. The simulation results demonstrate that the training rate of our method is obviously improved.]]>489147014811485<![CDATA[Second-Order Continuous-Time Algorithms for Economic Power Dispatch in Smart Grids]]>489148214921597<![CDATA[Control Performance Assessment for ILC-Controlled Batch Processes in a 2-D System Framework]]>489149315041329<![CDATA[Optimizing Computational Mission Operation by Periodic Backups and Preventive Replacements]]>489150515201648<![CDATA[Toward a Blind Quality Predictor for Screen Content Images]]>489152115301501<![CDATA[Adaptive Algorithms for Performance Improvement of a Class of Continuum Manipulators]]>489153115411960<![CDATA[Belief Rule Base Structure and Parameter Joint Optimization Under Disjunctive Assumption for Nonlinear Complex System Modeling]]>489154215542107<![CDATA[Efficient Ranking and Selection for Stochastic Simulation Model Based on Hypothesis Test]]>489155515652484<![CDATA[Passive Indoor Localization Based on CSI and Naive Bayes Classification]]>489156615771469<![CDATA[Discrete-Event Simulation and Integer Linear Programming for Constraint-Aware Resource Scheduling]]>ad hoc methods.]]>489157815932295<![CDATA[A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks]]>489159416061093<![CDATA[A Dynamic Logistic Dispatching System With Set-Based Particle Swarm Optimization]]>489160716212399<![CDATA[Black Hole Entropic Fuzzy Clustering]]>maximum-a-posteriori (MAP) framework, which in fact indicates that fuzziness and probability can co-jointly work in a collaborative rather than repulsive way. According to the proposed MAP framework for BHEFC, the fuzzifier ${m}$ , which is limited to be less than 1 rather than bigger than 1 in fuzzy ${c}$ -means, can be explained as the partition accuracy in the form of (1−${m}$ ) for a dataset, and the fuzzy memberships and clustering centers can be determined in a probabilistic inference way, by means of iterative sampling with the assumption of the Dirichlet distribution of the fuzzy memberships. The incremental version of the proposed fuzzy clustering model is also developed here. Experimental results on synthetic and real datasets and images for segmentation demonstrate the improved clustering results of the proposed algorithms over the comparison algorithms.]]>489162216362847<![CDATA[A Model-Based Hybrid Approach for Circuit Breaker Prognostics Encompassing Dynamic Reliability and Uncertainty]]>489163716481395<![CDATA[A Model for Hidden Behavior Prediction of Complex Systems Based on Belief Rule Base and Power Set]]>489164916551218<![CDATA[Introducing IEEE Collabratec]]>489165616562137<![CDATA[IEEE Systems, Man, and Cybernetics Society Information]]>489C3C3106<![CDATA[Information For Authors]]>489C4C4111