By William McCune
Complaints of the 14th overseas convention on automatic Deduction, held in Townsville, Australia, in July 1997. Paper.
Read or Download Automated Deduction - CADE-14: 14th International Conference on Automated Deduction, Townsville, North Queensland, Australia, July 13 - 17, 1997, Proceedings PDF
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Additional resources for Automated Deduction - CADE-14: 14th International Conference on Automated Deduction, Townsville, North Queensland, Australia, July 13 - 17, 1997, Proceedings
2) GA searches optimal points starting from a group (population) of points in the search space (potential solutions), rather than a single point. -K. Oh and W. Pedrycz Table 2. Comparison of simpliﬁed with linear fuzzy inference-based FNNs (a) In case of using Simpliﬁed fuzzy inference (Scheme I) R1 : If xi is Ai1 then Cyi1 = ωi1 .. Fuzzy rules Rj : If xi is Aij then Cyij = ωij .. Rz : If xi is Aiz then Cyiz = ωiz Structure z z fi (xi ) = Inference result FS FNN Linear fuzzy inference (Scheme II) R1 : · · · then Cyi1 = ωsi1 + xi ωi1 ..
Moreover the PNN structure is optimized by GAs, that is, a genetically optimized PNN (gPNN) is designed and the gPNN is applied to the consequence part of gHFNN. The gPNN that exhibits a ﬂexible and versatile structure is constructed on a basis of PNN [14,15] and GAs [8–10]. gPNN leads to the eﬀective reduction of the depth of the networks as well as the width of the layer, and the avoidance of a substantial amount of time-consuming iterations for ﬁnding the most preferred networks in conventional PNN.
In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN).
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