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Neural Processing Letters, Volume 4
Volume 4, Number 1, 1996
- Yoon-Kyung Choi, Ki-Hwan Ahn, Soo-Young Lee:
Effects of Multiplier Output Offsets on On-Chip Learning for Analog Neuro-Chips. 1-8 - Jianfeng Feng, David Brown:
A Novel Approach for Analyzing Dynamics in Neural Networks with Saturated Characteristics. 9-16 - José Aguilar
:
An Energy Function for the Random Neural Network. 17-27 - Graham P. Fletcher:
Adaptive Internal Activation Functions and Their Effect on Learning in Feed Forward Networks. 29-38 - Bernard Filliatre, Robert Racca:
Multi-Threshold Neurones Perceptron. 39-44 - Jung-Wook Cho:
Modular Neuro-Chip with On-Chip Learning and Adjustable Learning Parameters. 45-52 - Sungzoon Cho, Min Jang, James A. Reggia:
Effects of Varying Parameters on Properties of Self-Organizing Feature Maps. 53-59
Volume 4, Number 2, 1996
- Ralf Möller, Peter Paschke:
Simulation of Cortex-Like Neural Networks on a CNAPS SIMD Neurocomputer. 67-74 - Giovanna Castellano
Simplifying a Neuro-Fuzzy Model. 75-81 - Hyun Sook Rhee, Kyung-Whan Oh:
A Design and Analysis of Objective Function-Based Unsupervised Neural Networks for Fuzzy Clustering. 83-95 - Marc M. Van Hulle:
Combining Topographic Map Formation with Projection Pursuit Learning for Nonparametric Regression Analysis. 97-105 - A. Rusnák, Miroslav Fikar
Generalized Predictive Control Based on Neural Networks. 107-112
Volume 4, Number 3, 1996
- Keng-Shen Hung, K. Mervyn Curtis, John W. Orton:
The Influence of Photo-Detector Non-Linearity on the Multi-Functionality of an Opto-Electronic Cellular Neural Network. 119-125 - Gary G. Yen
Health Monitoring of Vibration Signatures in Rotorcraft Wings. 127-137 - Mark A. Girolami
A Temporal Model of Linear Anti-Hebbian Learning. 139-148 - Raffaele Calabretta, Riccardo Galbiati, Stefano Nolfi, Domenico Parisi:
Two Is Better Than One: A Diploid Genotype for Neural Networks. 149-155 - Johan A. K. Suykens
Modelling the Belgian Gas Consumption Using Neural Networks. 157-166 - Aristidis Likas, Konstantinos Blekas:
A Reinforcement Learning Approach Based on the Fuzzy Min-Max Neural Network. 167-172
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