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Titlebook: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization; Patricia Melin,Oscar Castillo,Janusz

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樓主: DUBIT
41#
發(fā)表于 2025-3-28 18:12:13 | 只看該作者
42#
發(fā)表于 2025-3-28 22:50:00 | 只看該作者
Evolution of Kernels for Support Vector Machine Classification on Large Datasetsther hand, a new method for improving the training time of support vector machines was recently developed. In this chapter, the new method is integrated in a kernel evolution scheme. Ten benchmark datasets are tested. Results indicate that the new method speeds up the evolution process when datasets are greater than 1000 instances.
43#
發(fā)表于 2025-3-28 23:11:58 | 只看該作者
Optimization of Ensemble Neural Networks with Fuzzy Integration Using the Particle Swarm Algorithm f Mackey Glass benchmark time series. Simulation results are presented for the optimization of the structure of the ensemble neural network with type-1 and type-2 fuzzy response integration and its optimization with genetic algorithms. The Simulation results show that the ensemble approach produces good prediction of the Mackey Glass time series.
44#
發(fā)表于 2025-3-29 03:54:21 | 只看該作者
45#
發(fā)表于 2025-3-29 08:59:58 | 只看該作者
Lecture Notes in Computer Sciencen are modified according to the found index values, obtaining all antecedents in the process. Afterwards, the Cuckoo Search algorithm is used to optimize the Interval Sugeno consequents of the Fuzzy Inference System. Some sample datasets are used to measure the output interval coverage.
46#
發(fā)表于 2025-3-29 15:26:07 | 只看該作者
47#
發(fā)表于 2025-3-29 15:33:52 | 只看該作者
https://doi.org/10.1007/978-3-030-44286-6ammar are tested with two well-known benchmark datasets of pattern recognition. Finally, neural networks derived from the proposed grammar are compared with other generated by similar grammars which were designed for the same purposed, the neural network design.
48#
發(fā)表于 2025-3-29 21:00:06 | 只看該作者
https://doi.org/10.1007/978-3-030-44286-6 global optimization ability, but also avoid the premature convergence problem. The Improved PSO Algorithm IPSO is applied to Neural Network to optimize the architecture. The results show that there is an improvement with respect to using the conventional PSO Algorithm.
49#
發(fā)表于 2025-3-30 02:22:26 | 只看該作者
50#
發(fā)表于 2025-3-30 04:57:18 | 只看該作者
1860-949X ithms. The fifth part presents diverse applications of nature-inspired optimization algorithms. The sixth part contains papers describing new optimization algorithms. The seventh part contains papers describing978-3-319-36961-7978-3-319-17747-2Series ISSN 1860-949X Series E-ISSN 1860-9503
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