Towards improving the efficiency of the fuzzy cognitive map classifier

Nonsynaptic Error Backpropagation in Long-Term Cognitive Networks

We introduce a neural cognitive mapping technique named long-term cognitive network (LTCN) that is able to memorize long-term dependencies between a sequence of input and output vectors, especially in those scenarios that require predicting the values of multiple dependent variables at the same time. The proposed technique is an extension of a recently proposed method named short-term cognitive network that aims at preserving the expert knowledge encoded in the weight matrix while optimizing the nonlinear mappings provided by the transfer function of each neuron. A nonsynaptic, backpropagation-based learning algorithm powered by stochastic gradient descent is put forward to iteratively optimize four parameters of the generalized sigmoid transfer function associated with each neuron. Numerical simulations over 35 multivariate regression and pattern completion data sets confirm that the proposed LTCN algorithm attains statistically significant performance differences with respect to other well-known state-of-the-art methods.

Uncertainty Propagation in Fuzzy Grey Cognitive Maps With Hebbian-Like Learning Algorithms

This paper is focused on an innovative fuzzy cognitive maps extension called fuzzy grey cognitive maps (FGCMs). FGCMs are a mixture of fuzzy cognitive maps and grey systems theory. These have become a useful framework for facing problems with high uncertainty, under discrete small and incomplete datasets. This paper deals with the problem of uncertainty propagation in FGCM dynamics with Hebbian learning. In addition, this paper applies differential Hebbian learning (DHL) and balanced DHL to FGCMs for the first time. We analyze the uncertainty propagation in eight different scenarios in a classical chemical control problem. The results give insight into the propagation of the uncertainty or greyness in the iterations of the FGCMs. The results show that the nonlinear Hebbian learning is the choice with less uncertainty in steady final grey states for Hebbian learning algorithms.