Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Soft Computing and Fuzzy Logic
IEEE Software
Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning
IEEE Transactions on Fuzzy Systems
The cascade-correlation learning: a projection pursuit learning perspective
IEEE Transactions on Neural Networks
Objective functions for training new hidden units in constructive neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Constructive feedforward neural networks using Hermite polynomial activation functions
IEEE Transactions on Neural Networks
Regression modeling in back-propagation and projection pursuit learning
IEEE Transactions on Neural Networks
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The ink drop spread (IDS) method is a modeling technique developed by algorithmically mimicking the information-handling processes of the human brain. This method has been proposed as a new approach to soft computing. IDS modeling is characterized by processing that uses intuitive pattern information instead of complex formulas, and it is capable of stable and fast convergences. This paper investigates the modeling ability of the IDS method based on three typical benchmarks. Experimental results demonstrated that the IDS method can handle various modeling targets, ranging from logic operations to complex nonlinear systems, and that its modeling performance is satisfactory in comparison with that of feedforward neural networks.