A competitive modular connectionist architecture
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Computational neuroscience
Soft computing in human-related sciences
Soft computing in human-related sciences
Control with words: the modular approach
Information Sciences—Informatics and Computer Science: An International Journal - Special issue computing with words
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Intelligent Adaptive Control: Industrial Applications
Intelligent Adaptive Control: Industrial Applications
Modular Neural Network Classifiers: A Comparative Study
Journal of Intelligent and Robotic Systems
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
Task decomposition through competition in a modular connectionist architecture
Task decomposition through competition in a modular connectionist architecture
MACLAW: A modular approach for clustering with local attribute weighting
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems (Studies in Fuzziness and Soft Computing)
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Adaptive mixtures of local experts
Neural Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
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We study the feasibility and the performance of modular design concept as applied to pattern profiling problems using artificial neural network. By decomposing the given pattern profiling problem into smaller modules, it is shown that comparable performance can be achieved with improvement on computation and design complexity. A survey of typical modular neural networks shows that large-scale nonlinear problems can alleviate its dimensionality curse with modular technique. Overview of modular neural networks based on how the problem is modularized through various decomposition and subsequent aggregation is given. A pattern recognition problem for aircraft trajectory prediction using NeuroFuzzy learning with a two stage modular learning design is presented. Decoupled data are used to train respective neural network modules. A genetic algorithm is used to aggregate all the learned modules so that it is ready for online pattern recognition purpose. As compared with the non-modular approach, the modular approach offers comparable prediction performance with significantly lower overall computation time. This study validates that modular design is a promising solution for large-scale soft computing problems.