Nonlinear inter-symbol interference leads to significant error rate in nonlinear communication and digital storage channel. In this paper, therefore, a novel recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A) is proposed for nonlinear channel equalization. The RT2FNN-A uses the interval asymmetric type-2 fuzzy sets and it implements the fuzzy logic sy...

This paper considers the use of neural networks for nonlinear state estimation, system identification and control. As a case study we use data taken from a nonlinear injection valve for a superheater attemporator at a power plant. One neural network is trained as a nonlinear simulation model of the process, then another network is trained to act as a combined state and parameter estimator for t...

This paper proposes a nonlinear oscillator network model for gray-level image segmentation suitable for massively parallel VLSI implementation. The model performs image segmentation in parallel using nonlinear analog dynamics. Because of the limited calculation precision in VLSI implementation, it is important to estimate the calculation precision required for proper operation. By numerical sim...

This paper presents a direct multivariable adaptive controller using neural network which adapts to the changing parameters of the multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. It base on the theory which a nonlinear multivariable systems to be controlled is divided a linear part and a nonlinear part. The controller parameters of the linear ...

−The accurate and reliable on-line estimation of product quality is an essential task for successful process operation and control. This paper proposes a new estimation method that extends the conventional linear PLS (Partial Least Squares) regression method to a nonlinear framework in a more robust manner. To handle the nonlinearities, nonlinear PLS based on linear PLS and neural network has b...

In this paper, we propose a new parallel algorithm that can help the maximum neural network escape from local minima for maximum cut problem. By adding a nonlinear self-feedback to the maximum neural network, the proposed parallel algorithm has richer and more flexible dynamics and can prevent the network from getting stuck at local minima. A large number of instances have been simulated to ver...

This paper presents a robust control scheme for nonlinear TCP time-delay dynamic network systems. By introducing the interference and time-delay to improve the nonlinear model, the objective of the work described here was to express various interference and time-delay factors in the actual network running environment. Using proper coordinate transformation, evaluation signal and energy storage ...