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Railway interlocking is a safety critical system because its incorrect functioning may cause serious consequences. Modeling of a reliable interlocking has become a challenging problem due to its inherent complexity and introduction of new technologies. In this paper, formal analysis of safety properties of moving block interlocking is presented preventing(More)
{ We apply evolutionary computations to Hopeld's neural network model of associative memory. Previously, we reported that a genetic algorithm can enlarge the Hebb rule associative memory by pruning some of overloaded Hebbian synaptic weights. In this paper, we present the genetic algorithm also evolves random synaptic weights to store some number of(More)
abstract We simulated an associative memory with mutually connected neural network, and successfully made the connection matrix learn some binary patterns only by means of genetic algorithm. Although the memory capacity is about 12 % of the number of neurons, the fact that it was made without any learning algorithm like Hebbian rule is very interesting. The(More)
We apply some variants of evolutionary computations to the Hopeld model of associative memory. In this paper, we use the Breeder Genetic Algorithm (BGA) to explore the optimal set of synaptic weights with respect to the storage capacity. We present the BGA has tremendous ability to search a solution in the massively multi-modal landscape of the synaptic(More)
We apply evolutionary algorithms to Hopeld model of as-sociative memory. Previously we reported that a genetic algorithm using ternary chromosomes evolves the Hebb-rule associative memory to enhance its storage capacity by pruning some connections. This paper describes a genetic algorithm using real-encoded chromosomes which successfully evolves overloaded(More)