Abstract
We propose a sequential learning neural network (SLNN) and analyze the learning and recognition performances of the SLNN. The proposed SLNN consists of the bounded weight adjustment algorithm and structure adjustment method. Through many experiments, it turns out that our proposed SLNN can not only learn the knowledge of samples in series efficiently but also has fast learning speed. As an actual example of our network we have succeeded in applying our SLNN to Asian corn borer forecasting.
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