Pii: S0893-6080(99)00034-9
نویسندگان
چکیده
The self-organization model with a conformal-mapping adaptation is studied in this work. This model is designed to provide conformal transformation to meet the conformality requirement in biological morphology and geometrical surface mapping. This model spans the network field in the input space where topological conformality is preserved. The converged network provides not only the organized clustering features of the input but also a specific mapping representation. This facilitates the Kohonen’s self-organization model in exploring the input in a continuous conformality sense. Simulations for morphing applications are described. q 1999 Elsevier Science Ltd. All rights reserved.
منابع مشابه
Pii: S0893-6080(98)00034-3
A multilayer recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic con...
متن کاملPii: S0893-6080(00)00062-9
This article gives an overview of the different functional brain imaging methods, the kinds of questions these methods try to address and some of the questions associated with functional neuroimaging data for which neural modeling must be employed to provide reasonable answers. q 2000 Published by Elsevier Science Ltd.
متن کاملSCAN: A Scalable Model of Attentional Selection
This paper describes the SCAN (Signal Channelling Attentional Network) model, a scalable neural network model for attentional scanning. The building block of SCAN is a gating lattice, a sparsely-connected neural network defined as a special case of the Ising lattice from statistical mechanics. The process of spatial selection through covert attention is interpreted as a biological solution to t...
متن کاملPii: S0893-6080(99)00042-8
This paper presents a theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network. The perceptron learning scheme is proposed to store sample patterns as the stable states in a generalized Hopfield network. We have obtained that n 2 1 and 2n are a lower and an upper bound of the asymptotic memory capacity of the network of n neurons, respectively, which shows th...
متن کاملPii: S0893-6080(99)00058-1
The aim of the paper is to investigate the application of control schemes based on “internal models” to the stabilization of the standing posture. The computational complexities of the control problems are analyzed, showing that muscle stiffness alone is insufficient to carry out the task. The paper also re-visits the concept of the cerebellum as a Smith’s predictor. q 1999 Elsevier Science Ltd...
متن کامل