Neocognitron's Parameter Tuning by Genetic Algorithms

Neocognitron's Parameter Tuning by Genetic Algorithms,10.1142/S012906579900054X,International Journal of Neural Systems,Daming Shi,Chunlei Dong,Daniel

Neocognitron's Parameter Tuning by Genetic Algorithms   (Citations: 8)
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The further study on the sensitivity analysis of Neocognitron is discussed in this paper. Fukushima's Neocognitron is capable of recognizing distorted patterns as well as tolerating positional shift. Supervised learning of the Neocognitron is fullled by training patterns layer by layer. However, many parameters, such as selectivity and receptive elds are set manually. Furthermore, in Fukushima's original Neocogni- tron, all the training patterns are designed empirically. In this paper, we use Genetic Algorithms (GAs) to tune the parameters of Neocognitron and search its reasonable training pattern sets. Four contri- butions are claimed: rst, by analyzing the learning mechanism of Fukushima's original Neocognitron, the correlations amongst the training patterns are claimed to aect the performance of Neocognitron, tuning the Neocognitron's number of planes is equivalent to searching reasonable training patterns for its supervised learning; second, a GA-based supervised learning of the Neocognitron is carried out in this way, searching the parameters and training patterns by GAs but specifying the connection weights by training the Neocognitron; third, other than traditional GAs which are unsuitable for the large searching space of training patterns set, the cooperative coevolution is incorporated to play this role; fourth, an eective tness function is given out when applying the above methodology into numeral recognition. The evolutionary computation in our initial experiments is implemented based on the original training pattern set, e.g. the individuals of the population are generated from Fukushima's original training pat- terns during initialization of GAs. The results prove that our correlation analysis is reasonable, and show that the performance of a Neocognitron is sensitive to its training patterns, selectivity and receptive elds, especially, the performance is not monotonically increasing with respect to the number of training patterns, and this GA-based supervised learning is able to improve Neocognitron's performance.
Journal: International Journal of Neural Systems - IJNS , vol. 9, no. 6, pp. 497-509, 1999
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