Proposal of chaotic steepest descent method for neural networks and analysis of their dynamics
Jun Tani · Electronics and Communications in Japan (Part III Fundamental Electronic Science) · 1992
Abstract Most of the neural network models based on the Lyapunov stability contain various problems such as the trap of the local minimum, and the limit of their dynamic performances has been pointed out. This paper attempts to provide a new dynamic performance to such a neural network model by introducing chaos dynamics. The features of the chaotic dynamic model proposed here is that the dynamical equation describing the trajectory on the energy curve has a periodically varying nonlinear resistance in the dissipation term. By iterating the stable and unstable phases, the chaotic transitions of the state can be realized. The proposed dynamic model is applied to the error backpropagation learning and the memory recall in the Hopfield‐type network, and the chaotic minimum transitions in the dynamic process are verified, it is verified further that the intermittent chaos generated by an appropriate parameter manipulation can introduce useful dynamic behaviors into the network, e.g., the flexible learning and the memory recall with a structure containing both stability and plasticity.