A neural network-based associative memory for storing complex patterns is proposed. Two variations of the model are proposed: (1) discrete model and, (2) continuous model. The latter approaches the former as a limit. A crude capacity estimate for the discrete model is made. Network weights can be calculated in one step using a complex outer-product rule or can be adjusted adaptively using a Heb...

In this paper we introduce novel geometric concepts, namely category regions, in the original framework of Fuzzy-ART (FA) and Fuzzy-ARTMAP (FAM). The definitions of these regions are based on geometric interpretations of the vigilance test and the F2 layer competition of committed nodes with uncommitted ones, that we call commitment test. It turns out that not only these regions have the same g...

We have studied the chaos control in chaotic neural network by limiting the phase space at varying time interval. It provides a controlled output patterns with different temporal periods depending upon the control parameters. The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behavior with a non–periodic associative memory. In the chaotic neural network, it i...

1 Abstract The ability of human beings to retrieve information on the basis of associated cues continues to elicit great interest among researchers. Investigations of how the brain is capable to make such associations from partial information have led to a variety of theoretical neu-ral network models that act as associative memories. Recently, several researchers have had signiicant success in...

in this paper, a bidirectional associative memory (bam) neural network, which consists of two neurons in the x-layer and three neurons in the y-layer, with two time delays will be studied. we conclude that under some assumptions, hopf bifurcation occurs when the sum of two delays passes through a critical value. a numerical example is presented to support our theoretical results.

there are three major strategies to form neural network ensembles. the simplest one is the cross validation strategy in which all members are trained with the same training data. bagging and boosting strategies pro-duce perturbed sample from training data. this paper provides an ideal model based on two important factors: activation function and number of neurons in the hidden layer and based u...

Neural networks are a powerful method for solving complex, "real world", modeling problems when traditional algorithms cannot be formulated. Most neural networks are implemented in programming languages that can represent data structures well such as C/C++, Java or Visual Basic. SAS is very good at processing longititudinal data. This paper will offer techniques and methods through implementati...

premature ventricular contraction (pvc) is one of the common cardiac arrhythmias. the occurrence of pvc is dangerous in people who have recently undergone heart. a pvc beat can easily be diagnosed by a doctor based on the shape of the electrocardiogram signal. but in automatic detection, extracting several important features from each beat is required. in this paper, a method for automatic dete...

precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

The complex valued neural networks are the extended version of conventional real valued neural networks Input and output signals weighting factors and neuron nonlinear functions are determined using complex number so that the information geometry of the network is constructed in complex space This feature is advantageously used especially for learning and expressing smooth dynamical attractors ...