A new encoding method for optimizing neural network structures is proposed. It is based on an indirect encoding method with variable length gene code. The search ability forfinding an optimal solution is higher than the direct encoding methods b a u s e redundant information in gene code is reduced and the search space is also reduced. The proposed method easily operates adding and deleting hidden units. The performance of the proposed method is evaluated through computer simulations.
According to recent knowledge of brain science it is suggested that there exists functions distribution, which means that specific parts exist in the brain for realizing specific functions. This paper introduces a new brain-like model called Learning Petri Network (LPN) that has the capability of functions distribution and learning. The idea is to use Petri net to realize the functions distribution and to incorporate the learning and representing ability of neural network into the Petri net. The obtained LPN can be used in the same way as a neural network to model and control dynamic systems, while it is distinctive to a neural network in that it has the capability of functions distribution. An application of the LPN to nonlinear crane control systems is discussed. It is shown via numerical simulations that the proposed LPN controller has superior performance to the commonly-used neural network one.
In this paper, Universal Learning Network(U.L.N.) is presented, which models and controls large scale complicated systems such as industrial plants, economic, social and life phenomena, and also a computing method of higher order derivatives of U.L.N. is derived in order to obtain learning ability. The basic idea of U.L.N. is that large scale complicated systems can be modeled by the network which consists of nonlinearly operated nodes and branches which may have arbitrary time delays including zero or minus ones. It has not been presented that the network such as U.L.N. is able to model and control naturally the large scale complicated systems which can be seen commonly in the social and physical worlds. It is shown that first order derivatives of U.L.N. with sigmoid functions and one sampling time delays correspond to the back propagation learning algorithm of the recurrent neural network.
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