By Miguel Filgueiras, Luis Damas, Nelma Moreira, Ana P. Tomas
This quantity is the complaints of the second one complex university on man made Intelligence (EAIA '90) held in Guarda, Portugal, October 8-12, 1990. the point of interest of the contributions is typical language processing. different types of topic are lined: - Linguistically prompted theories, awarded at an introductory point, comparable to X-bar idea and head- pushed word constitution grammar, - fresh tendencies in formalisms that allows you to be frequent to readers with a historical past in AI, corresponding to Montague semantics and state of affairs semantics. the themes have been selected to supply a balanced evaluate of an important rules in average language processing this present day. the various effects provided have been labored out very lately, are the topic of ongoing study, and feature no longer formerly seemed in ebook shape. This booklet could function a textbook: in truth its contents have been meant as lecture notes.
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Extra resources for Natural Language Processing: EAIA '90, 2nd Advanced School in Artificial Intelligence Guarda, Portugal, October 8–12, 1990 Proceedings
Example text
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).