Abstract
A high-order recurrent neuro-fuzzy system (HO-RNFS) is suggested in this paper, suitable for modeling highly complex nonlinear temporal processes. Feedback connections are introduced in the network including the context and the feedback nodes that serve as a means to memorize the firing history. The feedback paths in the firing loop are implemented through finite impulse response (FIR) synaptic filters leading to a higher-order network with enhanced temporal capabilities. The inference mechanism of the HO-RNFS is implemented by means of dynamic fuzzy rules where multiple steps-ahead predictions are provided for the internal variables, at the consequent part. Its structure is organized in an on-line fashion using a concurrent structure and parameter algorithm. Structure learning generates dynamically the input and output clusters of the rules, while parameter learning adjusts the network weights. The HO-RNFS is compared to the recurrent self-organizing neural fuzzy inference network (RSONFIN), being a special case of the suggested network. The experimental setup includes a benchmark temporal system and the adaptive noise cancellation problem. Extensive experimentation reveals that HO-RNFS exhibits superior speech enhancement performance as contrasted to RSONFIN, when complex noise passages are considered.
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