IEEE Transactions on Neural Networks

This paper presents a new neural network model for solving constrained variational inequality problems by converting the necessary and sufficient conditions for the solution into a system of nonlinear projection equations. Five sufficient conditions are provided to ensure that the proposed neural network is stable in the sense of Lyapunov and converges to an exact solution of the original problem ... View full abstract»

Cox's proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. In this paper, we propose a neural network model based on bootstrapping to estimate the survival function and predict the short-term survival at any time during the course of the disease. The bootstrapping for the neural network is introduced when selecting the op... View full abstract»

The purpose of this research is to develop a classifier capable of state-of-the-art performance in both computational efficiency and generalization ability while allowing the algorithm designer to choose arbitrary loss functions as appropriate for a give problem domain. This is critical in applications involving heavily imbalanced, noisy, or non-Gaussian distributed data. To achieve this goal, a k... View full abstract»

The advantages brought about by using classical linear control theory in conjunction with neural approximators have long been recognized in the literature. In particular, using linear controllers to obtain the starting neural control design has been shown to be a key step for the successful development and implementation of adaptive-critic neural controllers. Despite their adaptive capabilities, n... View full abstract»

This paper presents a new type of control framework for dynamical stochastic systems, called statistic tracking control (STC). The system considered is general and non-Gaussian and the tracking objective is the statistical information of a given target probability density function (pdf), rather than a deterministic signal. The control aims at making the statistical information of the output pdfs t... View full abstract»

This paper presents a new learning algorithm for multitask pattern recognition (MTPR) problems. We consider learning multiple multiclass classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is ldquoonli... View full abstract»

Learning classifier systems (LCSs) are rule-based inductive learning systems that have been widely used in the field of supervised and reinforcement learning over the last few years. This paper employs supervised classifier system (UCS), a supervised learning classifier system, that was introduced in 2003 for classification tasks in data mining. We present an adaptive framework of UCS on top of a ... View full abstract»

A neural network (NN) was developed in order to increase the distance range of a phase-shift laser range finder and to achieve surface recognition, by using two photoelectrical signals issued from the measurement system. The NN architecture consists of a multilayer perceptron (MLP) with two inputs, three neurons in the hidden layer, and one output. Depending on the application, the NN output has t... View full abstract»

In this paper, a new method for adaptive control of general nonlinear and non-Gaussian unknown stochastic systems has been proposed. The method applies the minimum entropy control scheme to decrease the closed-loop randomness of the output under an iterative learning control (ILC) basis. Both modeling and control of the plant are performed using dynamic neural networks. For this purpose, the whole... View full abstract»

In this paper, adaptive neural network (NN) tracking control is investigated for a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems in triangular control structure with unknown nonsymmetric dead zones and control directions. The design is based on the principle of sliding mode control and the use of Nussbaum-type functions in solving the problem of the completely unknown ... View full abstract»

This paper presents a new approach for learning in structured domains (SDs) using a constructive neural network for graphs (NN4G). The new model allows the extension of the input domain for supervised neural networks to a general class of graphs including both acyclic/cyclic, directed/undirected labeled graphs. In particular, the model can realize adaptive contextual transductions, learning the ma... View full abstract»

This paper investigates the structure of Boolean networks via input-state structure. Using the algebraic form proposed by the author, the logic-based input-state dynamics of Boolean networks, called the Boolean control networks, is converted into an algebraic discrete-time dynamic system. Then the structure of cycles of Boolean control systems is obtained as compounded cycles. Using the obtained i... View full abstract»

Fuzzy neural networks (FNNs) have been successfully applied to generate predictive rules for medical or diagnostic data. This brief presents an approach to detect premature ventricular contractions (PVCs) using the neural network with weighted fuzzy membership functions (NEWFMs). The NEWFM classifies normal and PVC beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs) us... View full abstract»

The recently proposed neural network rate control (NNRC) framework that achieves queueing delay and queue length regulation, is expanded to further guarantee fair allocation of network resources among competing sources. This is possible by introducing a novel algorithm that controls in a stable and adaptive manner the number of communication channels in each source. Simulation studies performed on... View full abstract»

This brief deals with the problem of global asymptotic stability for a class of delayed neural networks. Some new Lyapunov-Krasovskii functionals are constructed by nonuniformly dividing the delay interval into multiple segments, and choosing proper functionals with different weighting matrices corresponding to different segments in the Lyapunov-Krasovskii functionals. Then using these new Lyapuno... View full abstract»

"For original paper see O. De Jesus, .ibid., vol. 18, no. 1, p.14-27,(2007)",. In a recent paper, De Jesus proposed a general framework for describing dynamic neural networks. Gradient and Jacobian calculations were discussed based on backpropagation-through-time (BPTT) algorithm and real-time recurrent learning (RTRL). Some errors in the paper of De Jesus bring difficulties for other researchers ... View full abstract»

This note is a supplement to the previously published paper entitled "Stability analysis and the stabilization of a class of discrete-time dynamic neural networks" (Patan, 2007) and focuses on a complete formulation of the minimum distance projection. The undertaken analysis results in the procedure defining the homogeneous projection operator and returning the feasible network parameters. View full abstract»