In this paper, an unsupervised parallel segmentation approach using a fuzzy Hopfield neural network (FHNN) is proposed. The main purpose is to embed fuzzy clustering into neural networks so that on-line learning and parallel implementation for medical image segmentation are feasible. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentati...

A new iterative learning controller is proposed for a general unknown discrete time-varying nonlinear non-affine system represented by NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. The proposed controller is composed of an iterative learning neural identifier and an iterative learning controller. Iterative learning control and iterative learning identification ar...

In this paper, we present the optimized routing technique for mobile ad hoc network based on the Hopfield neural network and mobile agent technology with primary goal to find an optimal route. Mobile agent technique is used to share the information of network conditions. Here, we realized the proposed routing technique through two independent methods. They are both based on the Hopfield neural ...

Image restoration is a very important task in image processing. The Artificial Neural Network (ANN) approach was used to solve this problem, especially the Discrete Hopfield Network (DHN). This approach suffers from the fluctuation problem due to the use of the hard limit function as activation function. To overcome this shortcoming, we use in this work the Continuous Hopfield Network (CHN) tha...

In this paper, an identification method is proposed for discrete-time nonlinear systems using a Hopfield neural network (HNN) as a coefficient learning mechanism to obtain optimized coefficients over a set of Gaussian basis functions. The outputs of the HNN, which are coefficients over a set of Gaussian basis functions, are discretized to be a discrete Hopfield learning model and completely app...

In this paper, we present FPGA recurrent neural network systems with learning capability using the simultaneous perturbation learning rule. In the neural network systems, outputs and internal values are represented by pulse train. That is, analog recurrent neural networks with pulse frequency representation are considered. The pulse density representation and the simultaneous perturbation enabl...

In this paper, a novel color image hiding technique using spread grey-based competitive Hopfield neural network (SGHNN) and Hadamard Transform (HT) digital watermarking is proposed. The goal is to offer secure communications in the internet through compress the original color image and embedded into another disguise color image. Our method includes a spread-unsupervised competitive Hopfield neu...

In this paper, a new Hopfield-model net based on fuzzy possibilistic reasoning is proposed for the classification of multispectral images. The main purpose is to modify the Hopfield network embedded with fuzzy possibilistic -means (FPCM) method to construct a classification system named fuzzy-possibilistic Hopfield net (FPHN). The classification system is a paradigm for the implementation of fu...