Time is considered as an important factor in modeling and operation of dynamic systems. However, few studies have considered time factor in modeling and inference of fuzzy cognitive maps (FCMs), besides, no studies have dealt with time delay in learning of FCMs. Therefore, we propose a learning rule for temporal FCMs involving post- and pre-delay time by extending Oja's learning rule. We show the effectiveness of the proposed rule through simulations which solve a time-delayed chemical plant control problem.

We propose a learning algorithm for self-organizing neural networks to form a topology preserving map from an input manifold whose topology may dynamically change. Experimental results show that the network using the proposed algorithm can rapidly adjust itself to represent the topology of nonstationary input distributions.

This peper proposes a simplified model of the well-known two-neuron neural oscillator. By eliminating one of the two positive feedback synapses in the neural oscillator, learning for the in-phase control of the oscillator is shown to be achievable via a very simple learning rule. The learning rule is devised in such a way that only the plasticity of two synaptic weights are required. We demonstrate some examples of the synchronization learning to validate the efficiency of the learning rule, and finally by illustrating the dynamics of the synchronization learning and by using computer simulation, we show the convergence behavior and the stability of the learning rule for the two-neuron simple neural oscillator.