International Journal of Neural Systems · 1999 · 145 citations · 81 references
ComputingComputer ScienceArtificial IntelligenceComputational NeuroscienceMachine LearningDecision-making ModelsDeep LearningModular OrganizationNetwork ModularityNeural NetworksPattern RecognitionModular DesignModular Network OrganizationModular Network StructureModularityModularity In DesignNeural Network Modeling
Modular Neural Networks (MNNs) are a rapidly growing area within artificial neural network research. This survey examines the diverse motivations behind MNNs, including biological, psychological, hardware, and computational perspectives. The paper outlines the general stages of MNN design, covering task decomposition techniques, learning schemes, and multi‑module decision‑making strategies. The survey highlights the advantages and disadvantages of various MNN approaches, assesses their practical potential, and offers recommendations for future designs.
Modular Neural Networks (MNNs) is a rapidly growing field in artificial Neural Networks (NNs) research. This paper surveys the different motivations for creating MNNs: biological, psychological, hardware, and computational. Then, the general stages of MNN design are outlined and surveyed as well, viz., task decomposition techniques, learning schemes and multi-module decision-making strategies. Advantages and disadvantages of the surveyed methods are pointed out, and an assessment with respect to practical potential is provided. Finally, some general recommendations for future designs are presented.
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell B. Stinchcombe, Halbert White · Neural Networks · 1989 · 21.2K citations
Multilayer feedforward networks are universal approximators
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