Abstract
Aiming at the problems of the radial basis network model, this paper proposes a multimedia vocal singing automation evaluation network model, combined with the characteristics of multimedia modeling innovation design, and proposes a two-level comprehensive model. First of all, the theory and algorithm of analytic hierarchy process and radial basis function network are researched and analyzed, and the RBF is predicted for the mature area of multimedia development based on the three indicators of the total amount of classified vocals. The prediction scheme evaluation system is then used to fit the prediction data and influencing factors using the RBF network, and then the classified vocals are adjusted and synthesized hierarchically, and a multimedia vocal classification prediction model is established. Finally, this paper uses an example to verify the feasibility performance and prediction accuracy. Based on the above theory, the experiment uses VC++ 6.0 and Matlab 6.5 combined with database technology to initially realize the evaluation system and achieves a good evaluation effect. The simulation results show that three different algorithms are used to establish RBFO content prediction models. The correlation coefficient limit, root mean square error prediction, and relative analysis error (RED) reached 0.9937, 15.5095, and 8.216, respectively. At the same time, the evaluation results have high accuracy and credibility, which not only provide designers with ideas and improvement basis for innovative designs but also ensure design quality, improve design efficiency, and show that RBF networks have good generalization capabilities.
Highlights
Since the RBF network has strong self-organization, selflearning, and associative memory capabilities, it can obtain the weight of the importance of the evaluation target through the learning of existing samples, and it can weaken the human factor in the determination of the weight to ensure it. erefore, the current radial basis network model is widely used to solve the problems of actual prediction and program evaluation [1]
Its systematic research will lay the foundation for the development of computer-aided innovation design research based on multimedia vocal music to establish and improve the multimedia design theory and method system will help the scientific, standardized, and quantified multimedia design and evaluation
Based on RBF network multiobjective optimization and fuzzy mathematics evaluation method, this paper selects the appropriate membership function and weighting coefficient to improve the original networkand establishes the automatic evaluation of multimedia vocal singing with the network comprehensive score as the output value. e RBF network realizes the evaluation of multimedia vocal music quality and finds the best evaluation combination of multimedia vocal singing automation evaluation through optimization
Summary
Since the RBF network has strong self-organization, selflearning, and associative memory capabilities, it can obtain the weight of the importance of the evaluation target through the learning of existing samples, and it can weaken the human factor in the determination of the weight to ensure it. erefore, the current radial basis network model is widely used to solve the problems of actual prediction and program evaluation [1]. E land-use simulation method has the following advantages: it can make full use of the results of the system’s vocal music prediction and predict the spatial distribution of the future distribution network vocal music based on it; at the same time, the land-use simulation model and the vocal music model can be used to analyze in detail the factors affecting the growth of vocal music. It is suitable for multischeme research, can simulate the development of the community, has high prediction accuracy, and can meet the purpose of long-term planning. Exceed 75%); otherwise, it means that the membership function representing the fuzzy set is too flat, so that, in the actual control process, the change of variables is relatively slow
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