Artificial Intelligence Applications and Innovations: IFIP by M. R. Gholamian, S. M. T. Fatemi Ghomi, M. Ghazanfari

By M. R. Gholamian, S. M. T. Fatemi Ghomi, M. Ghazanfari (auth.), Daoliang Li, Baoji Wang (eds.)

International Federation for info Processing

The IFIP sequence publishes state of the art leads to the sciences and applied sciences of data and communique. The scope of the sequence contains: foundations of computing device technology; software program idea and perform; schooling; desktop functions in know-how; verbal exchange structures; structures modeling and optimization; info platforms; desktops and society; computers know-how; safety and defense in details processing structures; synthetic intelligence; and human-computer interplay. court cases and post-proceedings of referred foreign meetings in desktop technological know-how and interdisciplinary fields are featured. those effects usually precede magazine book and characterize the most up-tp-date learn. The imperative goal of the IFIP sequence is to inspire schooling and the dissemination and trade of data approximately all points of computing.

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Extra info for Artificial Intelligence Applications and Innovations: IFIP TC12 WG12.5 - Second IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI2005), September 7–9, 2005, Beijing, China

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51 REALKBNN Our proposed approach, RealKBNN is based on KB ANN. First, domain theory in the form of if-then-else rules for real-valued inputs is translated into the forms of neurons, weights and thresholds. The novelty of this technique is in its manipulation of weight and threshold parameters by eUminating the hidden layer encoding the conjunctive relationship. This step speeds up the training time and convergence time since eliminating a layer in the neural network reduces its complexity and lessens the amount of the computational work.

Via trial and error, a set of meme interactions causing a psychologically plausible dynamics of simulated mental states has been obtained. P". Simulation experiments confirmed that even in case of constant streams of input memes, the densities of the resulting streams may vary in time. This phenomenon takes place especially when the populations of resulting contradictory memes have an equal or almost equal "birth rate". e. dominate Working Memory) and, after a period of time, suddenly lose to the population of contradictory memes.

0 + 1. 0+1. 0 + 1. 0 + 1. 0 + 1. oj Figure 2. 3 The Learning Mechanism As previously mentioned, the learning mechanism performs the function of modifying the knowledgebase of a direct fuzzy controller so that the closed-loop system behaves like the reference model. The learning mechanism consists of two parts: a fuzzy inverse model and a knowledgebase modifier. The fuzzy inverse model performs the function of mapping necessary changes in the process output, and decides how to change the plant command inputs so that this deviation goes to zero.

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