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In this paper, we are concerned with the finite-time synchronization of a class of inertial neural networks with time delays. Without applying some finite-time stability theorems, which are widely applied to studying the finite-time synchronization for neural networks, by constructing two Lyapunov functions and using integral inequality method, two sufficient conditions on the finite-time synchronization for a class of inertial neural networks with time delays are derived. Considering that the method and research results of the finite-time synchronization are different from some existing works, this paper extends the works on the finite-time synchronization of neural networks. [ABSTRACT FROM AUTHOR]

It is my pleasure to report that we have a couple of very successful on-going special issues, including a Special Issue on neurodynamic systems for optimization and applications and a Special Issue on neural networks and learning systems applications in smart grid. They received 101 and 79 submissions, respectively. You can visit http://www.tnnls.org for the call for papers of other Special Issues. If you and your colleagues would like to propose a Special Issue for TNNLS, please follow the instructions posted at http://cis.ieee.org/tnnls to submit your proposals. Special Issue proposals are always welcome on timely and hot topics related to neural networks and learning systems. [ABSTRACT FROM AUTHOR]

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For a class of nontriangular nonlinear systems in presence of unknown disturbances, we propose a predictor-based neural dynamic surface control (PNDSC) strategy in this paper. This nontriangular system is transformed via the mean value theorem, and a predictor is then constructed. To avoid an algebraic loop problem, partial state vectors are employed as input signals of neural networks (NNs) for approximating unknown dynamics, and compensation items are designed to compensate for approximation errors from NNs. Different from the traditional NDSC, the PNDSC in this paper utilizes prediction errors to update learning parameters for improving NNs’ learning behaviors with overlarge adaptive gains. On the basis of improved NNs’ approximation behaviors, a predictor-based NNs disturbance observer (PNNDO) is constructed for compensation for external disturbances and approximation errors from NNs. Furthermore, with predictors, a normalization method of weights is developed to reduce the number of online learning parameters. On the basis of the aforementioned result, measurement noises are taken into account in our predictor-based neural control strategy. We employ predictor states, rather than measurement information paralyzed by noises, in design of our control strategy. This reduces high-frequency oscillations in control input. A Lyapunov-based stability analysis shows that all signals are ultimately bounded in the closed-loop system. Finally, the effectiveness of the proposed control strategy is verified by a numerical example and a permanent magnet brushless DC motor system. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) with large scales of artificial neurons are increasingly used in recognition and classification tasks. In power-constrained scenarios, the tradeoff between performance and hardware consumptions must be carefully evaluated before silicon tape-out. In this paper, we proposed a systematic approach to design ultra-low power NN system. This work is motivated by the facts that NNs are resilient to approximation in many of the computations and NNs are outputting statistical tensors which are acceptable to less-than-perfect results. We resort to the front-back end approach with a twofold aim: (1) a fast and accurate design approach is proposed by estimating the computing quality of low-power approximate adder arrays, and it is adopted to evaluate the neural network system; (2) a quality configurable engine with different approximation degrees while processing NNs is implemented. The proposed work is demonstrated with a comprehensive keyword spotting (KWS) system as an ultra-low power NN engine. The experimental environment is setup with ten keywords from the google speech command dataset (GSCD) using an industrial 22-nm ultra-low-leakage (ULL) process. Comparing to the state-of-the-art KWS processors, the proposed approximate NN engine can demonstrate over 60% improvement in power efficiency and $1.1\times $ area efficiency while achieving similar recognition accuracy. [ABSTRACT FROM AUTHOR]

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These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

In this paper, we first address the dynamics of the elegant multivalued quaternionic Hopfield neural network (MV-QHNN) proposed by Minemoto et al. Contrary to what was expected, we show that the MV-QHNN, as well as one of its variation, does not always come to rest at an equilibrium state under the usual conditions. In fact, we provide simple examples in which the network yields a periodic sequence of quaternionic state vectors. Afterward, we turn our attention to the continuous-valued quaternionic Hopfield neural network (CV-QHNN), which can be derived from the MV-QHNN by means of a limit process. The CV-QHNN can be implemented more easily than the MV-QHNN model. Furthermore, the asynchronous CV-QHNN always settles down into an equilibrium state under the usual conditions. Theoretical issues are all illustrated by examples in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a novel class of quaternion-valued memristive neural networks (QVMNNs) that considers both the spatial factor and Markov jump phenomenon is proposed, the finite/fixed-time anti-synchronization (F/FTAS) of which is investigated. It is worth mentioning that the considered master and slave systems are assumed to jump along two inconsistent Markov chains, which is a first attempt at the issue of the anti-synchronization of Markovian systems and may be more realistic than most existing Markovian systems’ models. Then, a suitable feedback controller is designed, such that the error system can be finite/fixed-time stable. Moreover, by integrating algebraic inequality technologies and Lyapunov theory, a new F/FTAS theorem can be obtained for the proposed systems. Finally, this paper provides two examples, so that the rationality, superiority, and practical value of the main results can be illustrated. [ABSTRACT FROM AUTHOR]

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These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Hyperdimensional (HD) computing is a promising paradigm for future intelligent electronic appliances operating at low power. This paper discusses tradeoffs of selecting parameters of binary HD representations when applied to pattern recognition tasks. Particular design choices include density of representations and strategies for mapping data from the original representation. It is demonstrated that for the considered pattern recognition tasks (using synthetic and real-world data) both sparse and dense representations behave nearly identically. This paper also discusses implementation peculiarities which may favor one type of representations over the other. Finally, the capacity of representations of various densities is discussed. [ABSTRACT FROM AUTHOR]

The stability property of delayed neural networks (NNs) along the whole delay axis is studied in this paper. Such a complete stability problem with respect to the delay parameter has not been addressed in the community of NNs. Most of the existing studies focus on the stability interval of delay starting from zero and are not applicable for the complete stability problem. In this paper, we will present some examples to show that there are various types of stability intervals for NNs, demonstrating the necessity of the complete stability analysis. We will adopt a frequency-sweeping approach to study delayed NNs in this paper. As a result, the complete stability problem with respect to delay for NNs can be systematically solved. The approach is applicable in the general case and simple to implement. Finally, some representative examples illustrate the approach. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

The article focuses on the format of the journal "IEEE Transactions on Neural Networks and Learning Systems" for the authors to submit research papers and mentions charges of the pages, manuscripts submission, and editing services offered by publication of the journal.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) in the stochastic environment were widely modeled as stochastic differential equations, which were driven by white noise, such as Brown or Wiener process in the existing papers. However, they are not necessarily the best models to describe dynamic characters of NNs disturbed by nonwhite noise in some specific situations. In this paper, general noise disturbance, which may be nonwhite, is introduced to NNs. Since NNs with nonwhite noise cannot be described by Itô integral equation, a novel modeling method of stochastic NNs is utilized. By a framework in light of random field approach and Lyapunov theory, the global asymptotic stability and stabilization in probability or in the mean square of NNs with general noise are analyzed, respectively. Criteria for the concerned systems based on linear matrix inequality are proposed. Some examples are given to illustrate the effectiveness of the obtained results. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a low-power memory-based computing architecture, called selective computing architecture (SCA). It consists of multipliers and an LUT (Look-Up Table)-based component, that is multi-context ternary content-addressable memory (MC-TCAM). Either of them is selected by input-data conditions in neural-networks (NNs). Compared with quantized NNs, a higher accurate multiplication can be performed with low-power consumption in the proposed architecture. If input data stored in the MC-TCAM appears, the corresponding multiplication results for multiple weights are obtained. The MC-TCAM stores only shorter length of input data, resulting in achieving a low-power computing. The performance of the SCA is determined by three physical parameters concerning the configuration of MC-TCAM. The power dissipation of the target NN can be minimized by exploring these parameters in the design space. The hardware based on the proposed architecture is evaluated using TSMC 65 nm CMOS technology and MTJ model. In the case of speech command recognition, the power consumption at the multiplication of the first convolutional layer in a convolutional NN is reduced by 67% compared to the solution relying only on multipliers. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

This paper provides the stability analysis for a model-free action-dependent heuristic dynamic programing (HDP) approach with an eligibility trace long-term prediction parameter ($\lambda $). HDP($\lambda $) learns from more than one future reward. Eligibility traces have long been popular in Q-learning. This paper proves and demonstrates that they are worthwhile to use with HDP. In this paper, we prove its uniformly ultimately bounded (UUB) property under certain conditions. Previous works present a UUB proof for traditional HDP [HDP($\lambda =0$)], but we extend the proof with the $\lambda $ parameter. By using Lyapunov stability, we demonstrate the boundedness of the estimated error for the critic and actor neural networks as well as learning rate parameters. Three case studies demonstrate the effectiveness of HDP($\lambda $). The trajectories of the internal reinforcement signal nonlinear system are considered as the first case. We compare the results with the performance of HDP and traditional temporal difference [TD($\lambda $)] with different $\lambda $ values. The second case study is a single-link inverted pendulum. We investigate the performance of the inverted pendulum by comparing HDP($\lambda $) with regular HDP, with different levels of noise. The third case study is a 3-D maze navigation benchmark, which is compared with state action reward state action, Q($\lambda $), HDP, and HDP($\lambda $). All these simulation results illustrate that HDP($\lambda $) has a competitive performance; thus this contribution is not only UUB but also useful in comparison with traditional HDP. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification. [ABSTRACT FROM AUTHOR]

Neurons in a network can be both active or inactive. Given a subset of neurons in a network, is it possible for the subset of neurons to evolve to form an active oscillator by applying some external periodic stimulus? Furthermore, can these oscillator neurons be observable, that is, is it a stable oscillator? This paper explores such possibility, finding that an important property: any subset of neurons can be intermittently co-activated to form a stable oscillator by applying some external periodic input without any condition. Thus, the existing of intermittently active oscillator neurons is an essential property possessed by the networks. Moreover, this paper shows that, under some conditions, a subset of neurons can be fully co-activated to form a stable oscillator. Such neurons are called selectable oscillator neurons. Necessary and sufficient conditions are established for a subset of neurons to be selectable oscillator neurons in linear threshold recurrent neuron networks. It is proved that a subset of neurons forms selectable oscillator neurons if and only if the real part of each eigenvalue of the associated synaptic connection weight submatrix of the network is not larger than one. This simple condition makes the concept of selectable oscillator neurons tractable. The selectable oscillator neurons can be regarded as memories stored in the synaptic connections of networks, which enables to find a new perspective of memories in neural networks, different from the equilibrium-type attractors. [ABSTRACT FROM AUTHOR]

This paper develops a new method for solving the optimal control tracking problem for networked control systems (NCSs), where network-induced dropout can occur and the system dynamics are unknown. First, a novel dropout Smith predictor is designed to predict the current state based on historical data measurements over the communication network. Then, it is shown that the quadratic form of the performance index is preserved even with dropout, and the optimal tracker solution with dropout is given based on a novel dropout generalized algebraic Riccati equation. New algorithms for off-line policy iteration (PI), online PI, and Q-learning PI are presented for NCS with dropout. The Q-learning algorithm adaptively learns the optimal control online using data measured over the communication network based on reinforcement learning, including dropout, without requiring any knowledge of the system dynamics. Simulation results are provided to show that the proposed approaches give proper optimal tracking performance for the NCS with unknown dynamics and dropout. [ABSTRACT FROM AUTHOR]

This paper presents the integration of reinforcement learning-based differential evolution (DE) with the cooperative coevolution (R-CCDE) method in a compensatory neuro-fuzzy controller (CNFC). The CNFC model employs compensatory fuzzy operations, which increase the adaptability and effectiveness of the controller. The R-CCDE method was used to determine an adequate control policy for nonlinear system problems. The evolution of a population involved the use of DE with cooperative coevolution to adjust CNFC parameters, and the fitness function of the R-CCDE method is used by a reinforcement signal to determine the controller that can be used to solve the control problem. This paper identified the best performing controller to solve nonlinear system problems. The simulation results of the proposed R-CCDE method were compared with those of various DE methods and the performance of the proposed R-CCDE method was superior to that of the other methods. [ABSTRACT FROM AUTHOR]

This paper provides a review of design approaches towards artificial intelligence (AI) System-on-Chip. AI algorithms have progressed over the past decades from perceptron-based neural network (NN) and neuro-fuzzy (NF) system to today’s deep neural network (DNN) and neuromorphic computing. Recent DNN hardware accelerators focus on energy-efficient integration of digital circuits to realize real-time DNN operation while neuromorphic processors deploy new memory technologies with analog computation for low power consumption. However, different design approaches can be applied to such processor implementation with their pros and cons. This paper reviews from the early processor designs for NN and NF in both mixed-mode and digital implementations to the recent DNN SoC designs that we have proposed for a decade. The former content deals with NN and NF processors used as a functional building block of a machine vision SoC, while the latter concentrates on integration of the whole DNN function. We also provide a discussion on the approaches, and provide perspective on future research directions. [ABSTRACT FROM AUTHOR]

The article focuses on the design, application and theory of the neural networks including contributions by the Transactions on Neural Networks and learning systems (TNNLS) like the use of Open Researcher and Contributor ID (ORCID) in journal scripts and Open Access (OA) manuscript submission.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]