Pattern recognition (recognizing a pattern from inputs) and recall (describing or predicting the inputs associated with a recognizable pattern) are essential for neural-symbolic processing and cognitive capacities. Without them the brain cannot interact with the world e.g.: form internal representations and recall memory upon which it can perform logic and reason. Neural networks are efficient,...

the aim of this study was to propose a method for improving the power of recognition and classification of thromboembolic syndrome based on the analysis of ‎ gene expression data using artificial neural networks. the studied method was performed on a dataset which contained data about 117 patients admitted to a hospital in durham in 2009. of all the studied patients, 66 patients were suffering ...

The paper introduces a mixture of auto-associative neural networks for speaker verification. A new objective function based on posterior probabilities of phoneme classes is used for training the mixture. This objective function allows each component of the mixture to model part of the acoustic space corresponding to a broad phonetic class. This paper also proposes how factor analysis can be app...

II. REVIEW OF STATE OF THE ART Abstract—Classification of musical genres gives a useful measure of similarity and is often the most useful descriptor of a musical piece. Principal Component Analysis (PCA) has been generally applied on raw music signals to capture the major components for each genre. As a large number of principal components are obtained for different genres, the purpose of appl...

Temporal sequence generation readily occurs in nature. For example performing a series of motor movements or recalling a sequence of episodic memories. Proposed networks which perform temporal sequence generation are often in the form of a modification to an auto-associative memory by using heteroassociative or time-varying synaptic strengths, requiring some pre-chosen temporal functions. Intra...

In this paper we study the problem of the occurrence of cycles in autoassociative neural networks. We call these cycles dynamic attractors, show when and why they occur and how they can be identi-ed. Of particular interest is the pseudo-inverse network with reduced self-connection. We prove that it has dynamic attractors, which occur with a probability proportional to the number of prototypes a...

ABSTRACT In the present paper, an effort has been made to compare and analyze the performance for pattern recalling with conventional hebbian learning rule and with evolutionary algorithm in Hopfield Model of feedback Neural Networks. A set of ten objects has been considered as the pattern set. In the Hopfield type of neural networks of associative memory, the weighted code of input patterns pr...

Cellular neural networks (CNNs) are one type of interconnected neural network and differ from the well-known Hopfield model in that each cell has a piecewise linear output function. In this paper, we present a multi-valued CNN model in which each nonlinear element consists of a multi-valued output function. The function is defined by a linear combination of piecewise linear functions. We conduc...

Many approaches to transform classification problems from nonlinear to linear by feature transformation have been recently presented in the literature. These notably include sparse coding methods and deep neural networks. However, many of these approaches require the repeated application of a learning process upon the presentation of unseen data input vectors, or else involve the use of large n...

This paper addresses the definition of the requirements for the design of a neural network associative memory, with on-chip training, in standard digital CMOS technology. We investigate various learning rules which are integrable in silicon, and we study the associative memory properties of the resulting networks. We also investigate the relationships between the architecture of the circuit and...