A Developed Local Polynomial Neuro-Fuzzy Model for Nonlinear System Identification

作者：Abdollahzade Majid^*; Kazemi Reza

来源：International Journal on Artificial Intelligence Tools, 2015, 24(3): 1550004.

DOI：10.1142/S0218213015500049

• Local polynomial neuro-fuzzy model
• LPMT
• nonlinear system identification

This paper proposes a local polynomial model tree (LPMT) learning technique for prediction and identification of nonlinear systems and processes. The proposed LPMT learning algorithm is applied to a local polynomial neuro fuzzy (LPNF) model, which includes local polynomial models (LPMs), as Taylor series expansion of any unknown function. The LPMT algorithm is established based on the concept of hierarchical binary tree and heuristically partitions the input space into smaller sub-domains through axis-orthogonal splits. The proposed learning algorithm starts from a single local polynomial model and then refines the LPNF model by increasing the degree of the worst-performing LPM or by further partitioning of the input space. During the learning procedure, the validity functions automatically form a partition of unity and therefore normalization side effects, e.g. reactivation and poor extrapolation performance, are prevented. The LPNF model, trained by the proposed LPMT algorithm, is used for prediction and identification of the nonlinear processes and systems in three case studies. The obtained results and comparisons to other methods revealed the promising performance of the proposed model.

• 出版日期2015-6