We investigate the memory structure and retrieval of the brain and propose a hybrid neural network of addressable and content-addressable memory which is a special database model and can memorize and retrieve any piece of information (a binary pattern) both addressably and content-addressably. The architecture of this hybrid neural network is hierarchical and takes the form of a tree of slabs w...

An edge preserving image compression algorithm based on an unsupervised competitive neural network is proposed. The proposed neural network, the called weighted centroid neural network (WCNN), utilizes the characteristics of image blocks from edge areas. The mean/residual vector quantization (M/RVQ) scheme is utilized in this proposed approach as the framework of the proposed algorithm. The edg...

Abstract A uni ed learning framework is proposed Its di erent special cases will automatically lead us to current existing major types of neural network learnings e g data clustering various PCA type self organizations and their localized extensions self organizing topological map as well as supervised learning for feedforward network and modular architecture Not only this new framework is usef...

In this Paper, a neural network image recognition system is used. The Neocognitron[8] in that system is used as feature extractor, then the feature are classified by using a multilayered feedforward network to generate recognition codes. Many neural learning algorithms are used to extract the feature, then comparison among them is presented. Finally a comparison between most active algorithms a...

We present an alternative algorithm to global fitting procedures to construct Par-ton Distribution Functions parametrizations. The proposed algorithm uses Self-Organizing Maps which at variance with the standard Neural Networks, are based on competitive-learning. Self-Organizing Maps generate a non-uniform projection from a high dimensional data space onto a low dimensional one (usually 1 or 2 ...

‎By p-power (or partial p-power) transformation‎, ‎the Lagrangian function in nonconvex optimization problem becomes locally convex‎. ‎In this paper‎, ‎we present a neural network based on an NCP function for solving the nonconvex optimization problem‎. An important feature of this neural network is the one-to-one correspondence between its equilibria and KKT points of the nonconvex optimizatio...

This paper presents the result of the TRECVID 2017 AVS task by kobe nict siegen team. Consisting of three research institutes Kobe University, NICT and University of Siegen. We submitted the following three runs. 1) kobe nict siegen D M 1: This run uses feature vectors extracted by a pre-trained convolutional neural network (CNN) as input for a small-scale multi-layer neural network called micr...

infiltration rate is one of the most important soil physical parameters and is a basic input data in irrigation and drainage projects. although, a number of theoretical or experimental based equations are presented to describe this phenomenon but the evaluation of some new sciences such as artificial neural networks, for prediction of the phenomenon can be investigated. generally, the infiltrat...

Recognition of interacting features has been a difficult task in many existing feature-recognition systems. The unique topological patterns of isolated features change drastically when they interact. Hence many surfacebased methods encounter problems in accommodating these changes in their generic feature definitions. Recently, much effort has been concentrated on the volumetric approach. Howev...