IEEE Transactions on Neural Networks

In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, ...

In recent years, approximate policy iteration (API) has attracted increasing attention in reinforcement learning (RL), e.g., least-squares policy iteration (LSPI) and its kernelized version, the kernel-based LSPI algorithm. However, it remains difficult ...

Multiplicative updates have been widely used in approximative nonnegative matrix factorization (NMF) optimization because they are convenient to deploy. Their convergence proof is usually based on the minimization of an auxiliary upper-bounding function,...

In this paper, a one-layer recurrent neural network is presented for solving pseudoconvex optimization problems subject to linear equality constraints. The global convergence of the neural network can be guaranteed even though the objective function is ...

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is ...

There are a number of spiking and bursting neuron models with varying levels of complexity, ranging from the simple integrate-and-fire model to the more complex Hodgkin–Huxley model. The simpler models tend to be easily implemented in silicon but yet ...

There are generally two approaches to the design of a neural fuzzy system: 1) design by human experts, and 2) design through a self-organization of the numerical training data. While the former approach is highly subjective, the latter is commonly ...

This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the ...

Spiking neural networks (SNNs) are considered to be computationally more powerful than conventional NNs. However, the capability of SNNs in solving complex real-world problems remains to be demonstrated. In this paper, we propose a substantial extension ...

An extension of the fast independent component analysis algorithm is proposed for the blind separation of both ${\BBQ}$-proper and ${\BBQ}$-improper quaternion-valued signals. This is achieved by maximizing a negentropy-based cost function, and is ...

In this paper, synchronization of continuous dynamical networks with discrete-time communications is studied. Though the dynamical behavior of each node is continuous-time, the communications between every two different nodes are discrete-time, i.e., ...

A lot of nonlinear embedding techniques have been developed to recover the intrinsic low-dimensional manifolds embedded in the high-dimensional space. However, the quantitative evaluation criteria are less studied in literature. The embedding quality is ...

In this paper, the exponential synchronization for a class of complex networks with finite distributed delays coupling is studied via periodically intermittent control. Some novel and useful criteria are derived by utilizing a different technique ...

We present a novel approach to multitask learning in classification problems based on Gaussian process (GP) classification. The method extends previous work on multitask GP regression, constraining the overall covariance (across tasks and data points) ...

Full ceramic bearings are considered the first step toward full ceramic, oil-free engines in the future. No research on full ceramic bearing fault diagnostics using acoustic emission (AE) sensors has been reported. Unlike their steel counterparts, ...

We introduce a wait-and-chase scheme that models the contact times between moving agents within a connectionist construct. The idea that elementary processors move within a network to get a proper position is borne out both by biological neurons in the ...

Fisher's linear discriminant (FLD) is one of the most widely used linear feature extraction method, especially in many visual computation tasks. Based on the analysis on several limitations of the traditional FLD, this paper attempts to propose a new ...

Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path ...

This paper analyzes the expected time evolution of an auto-regressive (AR) process using self-organized maps (SOM). It investigates how a SOM captures the time information given by the AR input process and how the transitions from one neuron to another ...

We present a new programmable neighborhood mechanism for hardware implemented Kohonen self-organizing maps (SOMs) with three different map topologies realized on a single chip. The proposed circuit comes as a fully parallel and asynchronous ...

This paper is concerned with the passivity and stability problems of reaction-diffusion neural networks (RDNNs) in which the input and output variables are varied with the time and space variables. By utilizing the Lyapunov functional method combined ...

Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF ...

This brief tackles the problem of learning over the complex-valued matrix-hypersphere ${\BBS}_{n,p}^{\alpha}({\BBC})$. The developed learning theory is formulated in terms of Riemannian-gradient-based optimization of a regular criterion function and is ...

By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying ...

This brief presents a dynamical system approach to vector quantization or clustering based on ordinary differential equations with the potential for real-time implementation. Two examples of different pattern clusters demonstrate that the model can ...