Consumer Decision‐Making Power Based on BP Neural Network and Fuzzy Mathematical Model

Abstract

In real life, because of the uncertainty of risk, incomplete information, perceived cost, and other factors, there are irrational behaviors in the decision-making power of consumers, so it is of great practical significance to study the decision-making power of consumers in the choice of countermeasures and personalized product recommendation. The purpose of this paper is to analyze the decision-making power of consumers based on the BP neural network and fuzzy mathematical model. First, the basic theory of artificial neural network and the concepts of set theory and fuzzy reasoning of fuzzy mathematics are described. Second, the behavior prediction model with the equal emphasis on rationality and irrationality and the integration of artificial neural network and fuzzy mathematics are constructed. The comments of a certain mobile phone are selected as the experimental objects to analyze the decision-making reasoning and prediction of individual consumers in the network and the decision-making reasoning of group consumers in the network, the experimental results show that through Mamdani reasoning, behavioral intention = 5.72 . Through the fuzzy set processing, it is finally determined that the consumer’s purchase intention is close to the VT mode, which is “very inclined.” In the first method, the user’s recognition rate of product C1 is 82%, and in the second method, the user’s recognition rate is 55%. The comparison of the two methods is in line with the expectation. The first method extracts the user’s emotion and evaluation information from the comments, fully considers the personalized needs of consumers, and is closer to the prediction results of the system.

Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases

“…With this function, we can output the quantified impact range, degree of fault, and consequences of the infringement, which results in a continuous function value. Using this value in conjunction with the previous classification information, as well as the per capita income level, we input it into a backpropagation (BP) neural network [25][26][27] to predict the verdict in reputation infringement cases. The entire fuzzy neural network structure is shown in the following diagram.…”

“…Using this value in conjunction with the previous classification information, as well as the per capita income level, we input it into a backpropagation (BP) neural network [25][26][27] to predict the verdict in reputation infringement cases. The entire fuzzy neural network structure is shown in the following diagram.…”

Optimization of the Process Parameters of Fully Mechanized Top-Coal Caving in Thick-Seam Coal Using BP Neural Networks

“…Among them, the BP neural network is the most widely used model in artificial neural networks, which is a multi-layer feedforward neural network trained according to the algorithm of error back propagation [26]. The BP neural network is composed of an input layer, hidden layer and output layer and based on Sigmod function for operation and application, and has a strong nonlinear mapping ability and flexible network results [27]. Therefore, we can establish a nonlinear evaluation model by using this technology to better solve the randomness of weight definition, and ensure the accuracy and scientificity of evaluation results and activities [28,29].…”