Time series forecasting with appetitive reward-based pseudo-outer-product fuzzy neural network

Abstract

Appetitive operant conditioning in Aplysia for feeding behavior via electrical stimulation of esophageal nerve contingently reinforced upon each spontaneous bite resulted in contingently reinforced animals acquiring operant memory. Analysis of the cellular and molecular mechanisms of the feeding motor circuitry revealed activity-dependent neuronal modulation occurs at interneurons that mediate the feeding behaviors, providing one evidence that interneurons are possible loci of plasticity and contribute a mechanism to memory storage in addition to memory storage contributed by activity-dependent synaptic plasticity. In this paper, an associative ambiguity correction-based neuro-fuzzy network called ARPOP-CRI(S), is trained based on an appetitive reward learning algorithm that is biologically inspired from the appetitive operant conditioning of feeding behavior in Aplysia. ARPOP-CRI(S) is evaluated and compared with other modelling techniques by employing benchmark time series data sets. Experimental results are encouraging and shows that ARPOP-CRI(S) is a viable modelling technique for time series forecasting.