Introduction to artificial neural systems
Introduction to artificial neural systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Modified high-order neural network for invariant pattern recognition
Pattern Recognition Letters
Fuzzy polynomial neural networks for approximation of the compressive strength of concrete
Applied Soft Computing
Expert Systems with Applications: An International Journal
Hybrid high order neural networks
Applied Soft Computing
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive high-order neural tree for pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An optimized instance based learning algorithm for estimation of compressive strength of concrete
Engineering Applications of Artificial Intelligence
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This study proposes a modular neural network (MNN) that is designed to accomplish both artificial intelligent prediction and programming. Each modular element adopts a high-order neural network to create a formula that considers both weights and exponents. MNN represents practical problems in mathematical terms using modular functions, weight coefficients and exponents. This paper employed genetic algorithms to optimize MNN parameters and designed a target function to avoid over-fitting. Input parameters were identified and modular function influences were addressed in manner that significantly improved previous practices. In order to compare the effectiveness of results, a reference study on high-strength concrete was adopted, which had been previously studied using a genetic programming (GP) approach. In comparison with GP, MNN calculations were more accurate, used more concise programmed formulas, and allowed the potential to conduct parameter studies. The proposed MNN is a valid alternative approach to prediction and programming using artificial neural networks.