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» Optimizing number of hidden neurons in neural networks
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IJCNN
2000
IEEE
15 years 11 months ago
On Derivation of MLP Backpropagation from the Kelley-Bryson Optimal-Control Gradient Formula and Its Application
The well-known backpropagation (BP) derivative computation process for multilayer perceptrons (MLP) learning can be viewed as a simplified version of the Kelley-Bryson gradient f...
Eiji Mizutani, Stuart E. Dreyfus, Kenichi Nishio
ICANN
2003
Springer
15 years 12 months ago
The Spike Response Model: A Framework to Predict Neuronal Spike Trains
We propose a simple method to map a generic threshold model, namely the Spike Response Model, to artificial data of neuronal activity using a minimal amount of a priori informatio...
Renaud Jolivet, Timothy J. Lewis, Wulfram Gerstner
GECCO
2003
Springer
153views Optimization» more  GECCO 2003»
15 years 12 months ago
SEPA: Structure Evolution and Parameter Adaptation in Feed-Forward Neural Networks
Abstract. In developing algorithms that dynamically changes the structure and weights of ANN (Artificial Neural Networks), there must be a proper balance between network complexit...
Paulito P. Palmes, Taichi Hayasaka, Shiro Usui
CJ
2008
108views more  CJ 2008»
15 years 6 months ago
Computing with Time: From Neural Networks to Sensor Networks
This article advocates a new computing paradigm, called computing with time, that is capable of efficiently performing a certain class of computation, namely, searching in paralle...
Boleslaw K. Szymanski, Gilbert Chen
ICANNGA
2007
Springer
153views Algorithms» more  ICANNGA 2007»
16 years 27 days ago
Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem
This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (G...
Daniel Rivero, Juan R. Rabuñal, Julian Dora...