Introduction to artificial neural systems
Introduction to artificial neural systems
The recursive neural network and its applications in control theory
Computers and Electrical Engineering - Special issue on neural networks and fuzzy logic: theory and applications in robotics and manufacturing
Structure optimization of fuzzy neural network by genetic algorithm
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Modified high-order neural network for invariant pattern recognition
Pattern Recognition Letters
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
Expert Systems with Applications: An International Journal
Evolutionary fuzzy hybrid neural network for project cash flow control
Engineering Applications of Artificial Intelligence
A Hybrid Higher Order Neural Classifier for handling classification problems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using weighted genetic programming to program squat wall strengths and tune associated formulas
Engineering Applications of Artificial Intelligence
Modular neural network programming with genetic optimization
Expert Systems with Applications: An International Journal
Genetic programming for predicting aseismic abilities of school buildings
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Improving analytical models of circular concrete columns with genetic programming polynomials
Genetic Programming and Evolvable Machines
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Neural networks (NNs) represent a familiar artificial intelligence approach widely applied in many fields and to a wide range of issues. The back propagation network (BPN) is one of the most well-known NNs, comprising multilayer perceptrons (MLPs) with an error back propagation learning algorithm. BPN typically employs associate multiplicative weightings for layer connections. For single connections, BPN combines neuron inputs linearly to neuron outputs. In this study, the author develops and embeds high order connections (exponent multipliers) into the BPN. The resultant proposed hybrid high order neural network (HHONN) is intended to be applicable to both linear and high order connections. HHONN allows an additional connection type for BPN, which permits BPN to adapt to different scenarios. In this paper, learning equations for both weighting and high order connections are introduced in their general forms. A feedforward neural network with a topology of two hidden layers and one high order connection was developed and studied to confirm the improved performance of developed HHONN models. Case studies, including two basic tests (a function approximation and the TC problem) and squat wall strength learning, were used to verify HHONN performance. Results showed that, when the high order connection was employed anywhere except the eventual connection, HHONN delivered better results than achievable using traditional BPN. Such results show that HHONN successfully introduces high order connections into BPN.