The CMAC network performs a locally weighted approximation of functions by means of some basis functions. The input space is divided into small, overlapped ...
The consideration of a representative example shows how the parallelism can be used to design a GCMAC adapted to its particular application.
Recommendations · Fourier neural networks: A comparative study. We review neural network architectures which were motivated by Fourier series and integrals and ...
Fuzzy system and CMAC network with B-spline membership/basis functions are smooth approximators · Fourier analysis of the generalized CMAC neural network.
Generalizing CMAC architecture and training - ResearchGate
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In this paper, we propose a generalized CMAC (GCMAC) network that considers different degrees of generalization for each input. Its representation abilities are ...
Jan 8, 2022 · Fourier series are universal approximators of continuous functions and that's something proven in most analysis courses.
Missing: CMAC | Show results with:CMAC
Fourier analysis of the generalized CMAC neural network · F. González-SerranoA. Figueiras-VidalAntonio Artés-Rodríguez. Computer Science. Neural Networks. 1998.
This neural network is based on the topological structure of the multiple Fourier series, and is shown to be free of local minima. The global stability of the ...
The learning algorithm is formulated as a matrix iteration scheme, the convergence of which can be proved by a) standard matrix theory and b) Fourier analysis.
He stated that CMAC is a memory management technique which causes similar inputs to tend to generalize so as to produce similar outputs, and dissimilar inputs ...
Missing: Fourier analysis