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• We propose fuzzy reinforced polynomial neural networks based on PNN architecture. • We propose fuzzy reinforced polynomial neurons (FRPNs) in the design of FRPNNs. • The proposed FRPNNs is a generalization of hybrid predictors (HPs). In the field of dynamic system identification and prediction, linear models (e.g., autoregressive models), nonlinear models (namely, neural networks models), and hybrid predictors (HPs) that are a hybridization of linear and nonlinear models have been proposed in the past. However, they are not completely free from limitations: they exhibit difficulties to describe high-order nonlinear relations between input and output variables. In this study, we propose fuzzy reinforced polynomial neural networks (FRPNNs), which are polynomial neural network architecture-based on fuzzy reinforced polynomial neurons (FRPNs) to overcome this limitation. The proposed FRPNs that consist of approximation part (AP) and compensation part (CP) arise as novel HPs. Here the CP for modeling nonlinear patterns can be regarded as forming the reinforced part for the AP that aims at capturing linear patterns, while AP is the linear polynomial neuron used in the conventional polynomial neural networks. In some sense, the overall FRPNNs are essentially generalized polynomial neural network architecture with novel HPs. The parameters considered in the design of the proposed fuzzy reinforced polynomial neural networks are optimized with the aid of the particle swarm optimization (PSO). The performance of FRPNNs is discussed involving time series and system identification datasets. Experimental results demonstrate that the proposed FRPNNs achieve at most the accuracy of 43.6% higher in comparison with the accuracy produced by some classical models reported in the literature. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we introduce a new design methodology of granular-oriented self-organizing Hybrid Fuzzy polynomial neural networks (HFPNN) that is based on multi-layer perceptron with context-based polynomial neurons (CPNs) or polynomial neurons (PNs). In contrast to the typical architectures encountered in polynomial neural networks (PNN), our main objective is to develop a design strategy of HFPNN as follows: (a) The first layer of the proposed network consists of context-based polynomial neuron (CPN). Here CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of context-based fuzzy c-means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data (input variables) while the formation of the clusters here is guided by a collection of some predefined fuzzy sets (so-called contexts) specified in the output space. (b) The proposed design procedure being applied to each layer of HFPNN leads to the selection of the preferred nodes of the network (CPNs or PNs) whose local characteristics (such as the number of contexts, the number of clusters, a collection of the specific subset of input variables, and the order of the polynomial) can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of the performance of the proposed HFPNN, we use well-known machine learning data coming from the machine learning repository. [Copyright &y& Elsevier]