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Discover the innovative concept of Granular Neural Networks, introduced by W. Pedrycz and G. Vukovich in 2001, offering an evolution from traditional neural networks. Granular Networks employ granular weights and interval outputs, providing a flexible and efficient approach to processing information. By implementing Interval Operations Architecture, the network can operate within specific intervals, enhancing precision and adaptability. Explore different levels of granularity in weight assignment such as uniform ε value allocation or varied ε values for disparate connections. Enhance network performance through the application of Fitness Function, Single-objective PSO Experiment, and adapt to diverse computation requirements.
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Granular Neural Networks: Concepts and Development Schemes Mingli Song and Witold Pedrycz IEEE Trans. On NNLS vol.24 2013
What is granular networks? • Introduced by W. Pedrycz and G. Vukovich in Neurocomputing, 2001 • Traditional neural networks • numeric weights • single output • Granular neural networks • granular weights • interval output
Architecture Y=[y-, y+] k Oj=[oj-, oj+] Wj=[Wj-, Wj+] m 1 j Wji=[Wji-, Wji+] i 1 n X1 Xi Xn
Granularity • ε: values in unit interval • wji-=wji-ε-|wji|, wji+=wji+ε+|wji| • Allocation of information granularity: • C1: All weights use the same value of ε. ε- = ε+= ε/2 • C2: All weights use the same value of ε. ε-+ ε+= ε • C3: Different granularity (εi) for different connections. εi- = εi+= εi/2 • C4: Different granularity (εi) for different connections. εi-+ εi+= εi • C5: Randomly assigned.
