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- W2910040710 abstract "Artificial neural network (ANN) introduces different types of neural network structures and has been applied successfully in diverse domains of real-world problems. Among various available architectures, the feedforward neural networks (FNNs) are comparatively simple. The flexible structure and availability of good learning algorithms makes FNNs very popular. After 1980s, when the age of ANN started again, researchers identified that there is no formal straight forward approach for modeling optimal FNNs. Optimal FNNs may be viewed as: optimal weights, optimal hidden layers, optimal hidden neurons, and optimal learning algorithm, and so on. The important purpose of optimizing FNN is to enhance its generalized performance. This chapter aims to cover a wider range of FNN optimization approaches with emphasis on nature inspired algorithms." @default.
- W2910040710 created "2019-01-25" @default.
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- W2910040710 date "2019-01-01" @default.
- W2910040710 modified "2023-10-02" @default.
- W2910040710 title "Optimization of ANN Architecture: A Review on Nature-Inspired Techniques" @default.
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- W2910040710 doi "https://doi.org/10.1016/b978-0-12-816086-2.00007-2" @default.
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