Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.

Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA-MLP-NN)

Traditional symphony performances need to obtain a large amount of data in terms of effect evaluation to ensure the authenticity and stability of the data. In the process of processing the audience evaluation data, there are problems such as large calculation dimensions and low data relevance. Based on this, this article studies the audience evaluation model of teaching quality based on the multilayer perceptron genetic neural network algorithm for the data processing link in the evaluation of the symphony performance effect. Multilayer perceptrons are combined to collect data on the audience’s evaluation information; genetic neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all vocal data of the symphony performance process and effects according to different characteristics and expressions of the audience evaluation. Changes are analyzed and evaluated accurately. The experimental results show that the performance evaluation model of symphony performance based on the multilayer perceptron genetic neural network algorithm can be quantitatively evaluated in real time and is at least higher in accuracy than the results obtained by the mainstream evaluation method of data postprocessing with optimized iterative algorithms as the core 23.1%, its scope of application is also wider, and it has important practical significance in real-time quantitative evaluation of the effect of symphony performance.

Optimization and Simulation of Manuscript Management System Based on Fuzzy Genetic Neural Network

Manuscript management plays an important role in the whole periodical industry. Journals and magazine society receive different types of contribution documents from all over the world. Many submission files are mostly transmitted by e-mail, but there are some hidden disadvantages in e-mail. In view of this situation, this paper studies the establishment and optimization simulation of manuscript management system based on fuzzy genetic neural network (FGNN). On the basis of genetic neural network, combined with the advantages of FNN neural network algorithm, a FGNN structure is established to optimize the system, which is helpful to the learning and expression ability of the whole system. The results show that the FGNN can extract and express the structured data, and the submission management system runs faster. Then, UML modeling and PHP framework are used to realize the design and establishment of the system, and the simulation model of contribution system is constructed. The innovation of this study is to combine genetic neural network with FNN neural network, which can query, manage, and classify manuscripts quickly. It also improves the complex submission system and optimizes the submission process. The whole journal editing work has been effectively improved.

Accurate Solar Cell Modeling via Genetic Neural Network-Based Meta-Heuristic Algorithms

Accurate solar cell modeling is essential for reliable performance evaluation and prediction, real-time control, and maximum power harvest of photovoltaic (PV) systems. Nevertheless, such a model cannot always achieve satisfactory performance based on conventional optimization strategies caused by its high-nonlinear characteristics. Moreover, inadequate measured output current-voltage (I-V) data make it difficult for conventional meta-heuristic algorithms to obtain a high-quality optimum for solar cell modeling without a reliable fitness function. To address these problems, a novel genetic neural network (GNN)-based parameter estimation strategy for solar cells is proposed. Based on measured I-V data, the GNN firstly accomplishes the training of the neural network via a genetic algorithm. Then it can predict more virtual I-V data, thus a reliable fitness function can be constructed using extended I-V data. Therefore, meta-heuristic algorithms can implement an efficient search based on the reliable fitness function. Finally, two different cell models, e.g., a single diode model (SDM) and double diode model (DDM) are employed to validate the feasibility of the GNN. Case studies verify that GNN-based meta-heuristic algorithms can efficiently improve modeling reliability and convergence rate compared against meta-heuristic algorithms using only original measured I-V data.