Design of Neural Networks for Time Series Prediction Using Case-Initialized Genetic Algorithms
One of the major objectives of time series analysis is the design of time series models, used to support the decision-making in several application domains. Among the existing time series models, we highlight the Artificial Neural Networks (ANNs), which offer greater computational power than the classical linear models. However, as a drawback, the performance of ANNs is more vulnerable to wrong design decisions. One of the main difficulties of ANNs design is the selection of an adequate networks architecture. In this work, we propose the use of Case-Initialized Genetic Algorithms to help in the ANNs design. We maintain a case base in which each case associates a time series to a wellsucceeded neural network used to predict it. Given a new time series, the most similar cases are retrieved and their solutions are inserted in the initial population of the Genetic Algorithms (GAs). Next, the GAs are executed and the best generated neural model is returned. In the undergone tests, the Case-Initialized GAs presented a better generalization performance than the GAs with random initialization. We expect that the results will be improved as more cases are inserted in the base.
Ricardo Bastos Cavatcante Prud(e)ncio Teresa Bernarda Ludermir
Centre de Inform(a)tica, Universidade Federal de Pernambuco Caixa Postal 7851 - CEP 50732-970 - Recife (PE) - Brasil
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
1028-1033
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）