量子神経モデルの記述とファジィ理論

抄録

We have proposed the method of the neural network based on quantum theory (wave equation and path integrals) of polaritons, and made some relation’s tools and descriptions for calculations for arbitrary neural circuits developed. The most important difference between the common (classical) neural network and quantum one existed in whether there were interferences between both systems. And concretely we showed how those quantum methods, whose system contained much interference, were applied to the Bayes’ theory, entropy of information theory, and the two-step neural network of multi channels. Moreover, when we attempt to practice that calculation on classical fuzzy probability and quantum amplitude, we immediately find that fuzzy probability is equivalent to Choquet integral. However, we recognize the difference between Choquet integral and path integral. As Choquet integral is always real number, but quantum integral means complex number. Thus, Choquet integral has sometimes divergence of integral values in spite of finite integral value of quantum computation.