Synaptic Learning of Long-Term Cognitive Networks with Inputs

Abstract

In contrast with the extense variety of machine learning algorithms, to fully automate the reasoning process, only a few can take advantage of the expert knowledge. Fuzzy Cognitive Maps (FCMs) are neural networks that can naturally integrate this kind of knowledge in the inference process. Nevertheless, FCMs have serious drawbacks difficult to overcome from the absence of an intrinsically learning algorithm or limited prediction horizon of the activation space of the neurons. Recently, some variants of the FCMs like Short-Term Cognitive Networks (STCN) and Long Term Cognitive Networks (LTCN) have been proposed to solve this problems. In this paper, we propose a new neural network model as a variant of LTCNs called Long-Term Cognitive Networks with Inputs (LTCNIs). A new kind of input neuron which is not present in the traditional FCMs approach or the derived algorithms STCNs and LTCNs is introduced, in order to model inputs like energy or mass in physical systems. The performance of the method, is discussed through the modeling of a passive circuit problem. As a second contribution, a new flexible reasoning strategy, which preserves the expert knowledge through the synaptic learning is presented. A synaptic learning based on a gradient descent method is implemented limited by a set of restrictions that preserves the model semantics.