Description

A neural network is a computer system modeled on the nerve tissue and nervous system. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks have diverse applications in computer graphics, artificial intelligence (AI), machine & deep learning, chemistry, material science, among others. Research on structural properties and their classification of favorable or unfavorable for neural networks has been started recently. Employing tools from mathematics and specifically graph theory has been a key research direction in this area. Different graph-theoretic parameters have potential applications in studying topological properties of neural networks.

The effective estimation of the vibration of spacecraft’s is frontier issues in the aerospace industry. But the vibration of aerospace components such as space solar panels and flexible manipulators under large overall rotating motion is usually very complex due to strong nonlinearity and often requires a lot of repetitive and burdensome calculations. Artificial neural network (ANN) is composed of interconnected neurons, and a surrogate model can be established to effectively predict the mechanical characteristics. In addition, the computational efficiency will be greatly improved if the object is changed from the model that is established by commercial finite element method (FEM) software or numerical calculations to the surrogates. However, the stochasticity of the estimation by single neural network is inevitable, and some neural networks much rely on the initial weight and threshold. This paper will implement a new combinatorial strategy that relies on multiple neural network models, which have different activation functions and regression feedback processes based on reasonable selection of sample points. To solve the elongated training time led by comparing these neural networks, Genetic Algorithm (GA) is used to optimize the iteration speed of one neural network when finding the optimal output value. Thus, better estimation accuracy will be achieved by designing an algorithm to combine these combinatorial neural networks of surrogates (CNNS).

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With Regards
Alexei
Journal Coordinator
Journal of Annals of Behavioural Science