원문정보
초록
영어
Aesthetic factors are an essential part of farm machinery development and design. In this paper, we take seeding machinery, typical farm machinery, as an instance and establish an aesthetic evaluation model for seeding machinery based on RBF neural network to predict design effects, which will provide important evidence to intelligent design of seeding machinery. Furthermore, aesthetic characteristic elements of seeding machinery are analyzed to establish an evaluation index system that is classified into three levels, of which the first-level index include technical and formal beauty, the second-level index contains beauty of function, material, shape and color and the third-level index comprises 17 factors. RBF neural network is employed to establish a mathematical model, where input layer is composed of 17 low-level evaluation index values and output layer is the comprehensive evaluation values of aesthetics by experts. Training and verification of 22 samples found that predictive effects of RBF neural network-based model on the evaluation model of seeding machinery modeling are superior to BP network-based prediction model, for it can better deal with uncertainties.
목차
1. Introduction
2. Research Frameworks
3. Establishment of Aesthetic Evaluation Standard
3.1. Aesthetic Characteristic Analysis of Seeding Machinery
3.2. Evaluation Indexes of Seeding Machinery
4. Aesthetic Model based on RBF Neural Network
4.1. Principles of BP
4.2. Working Principles of RBF
4.3. Aesthetic Evaluation Model based on RBF Neural Network
5. Simulation Experiment
5.1. Sample Data Acquisition
5.2. Result Analysis of Simulation Test
6. Conclusions
References