Search

The reasons for consumers' resale behavior are complex and sometimes diverse, and the investigation of consumer resale behavior is not a simple matter. Therefore, only through a lot of investigation and inquiry can we reach relevant conclusions. Based on machine learning and BP neural network, this paper constructs a consumer online resale behavior measurement model. The contraction-expansion factor can balance the global search and local search capabilities in different iteration periods, and the differential evolution operator is introduced to solve the problem of lack of population diversity. After building the model, this study collects data through questionnaires, and combines neural network training models to take data training and data prediction. In addition, this study compares and analyzes real data with predicted data, and visually displays the comparison results through statistical graphs. The results show that the method proposed in this paper has certain effects and can provide theoretical references for subsequent related research. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) has been well applied in pattern recognition, classification and machine learning thanks to its high performance. Most ANNs are designed by a static structure whose weights are trained during a learning process by supervised or unsupervised methods. These training methods require a set of initial weights values, which are normally randomly generated, with different initial sets of weight values leading to different convergent ANNs for the same training set. Dealing with these drawbacks, a trend of dynamic ANN was invoked in the past year. However, they are either too complex or far from practical applications such as in the pathology predictor in binary multi-input multi-output (MIMO) problems, when the role of a symptom is considered as an agent, a pathology predictor's outcome is formed by action of active agents while other agents' activities seem to be ignored or have mirror effects. In this paper, we propose a new dynamic structural ANN for MIMO problems based on the dependency graph, which gives clear cause and result relationships between inputs and outputs. The new ANN has the dynamic structure of hidden layer as a directed graph showing the relation between input, hidden and output nodes. The properties of the new dynamic structural ANN are experienced with a pathology problem and its learning methods' performances are compared on a real well known dataset. The result shows that both approaches for structural learning process improve the quality of ANNs during learning iteration. [ABSTRACT FROM AUTHOR]