Ahşap Kompozit Malzemelerin Mekanik ve Fiziksel Özelliklerine göre Tahmininde Radyal Temelli Fonksiyon Sinir Ağının Kullanımı

Abstract

Knowing the mechanical and physical properties of a material is the most important criteria for engineers and designers interested in determining the intended use of the material. The prediction of wood composite materials based on their mechanical and physical properties plays an important role in their future application. In this study, radial basis function network approach was employed for prediction according to mechanical and physical properties of wood composite materials such as particleboard, fiberboard, oriented strand board and plywood, which have widespread use in the furniture industry and construction sector. Four physical and mechanical properties were used as the board density, bending strength, bending elastic modulus and tensile strength in the prediction of the wood composite materials. This study will assist wood composite users in the selection of wood composite materials that will provide the mechanical and physical properties determined in advance for any construction. Moreover, the present study will fill this gap in literature.

Application of Improved Sliding Mode and Artificial Neural Networks in Robot Control

“…The RBF neural network has numerous applications, including function approximation, classification, and system control. Its strengths lie in its simple design structure, ease of training, fast convergence, and the ability to effectively fit any nonlinear function without falling into local optimum solutions [31]. The [5-7-1] structure of the RBF neural network is utilized to estimate the components a i|i=1,2,3 in the matrix A W of Equation ( 8) as shown in Figure 3.…”