Vitaliy Kolodyazhniy


A multilayer spline-based fuzzy neural network (MS-FNN) is proposed. It is based on the concept of multilayer perceptron (MLP) with B-spline receptive field functions (Spline Net). In this paper, B-splines are considered in the framework of fuzzy set theory as membership functions such that the entire network can be represented in form of fuzzy rules. MS-FNN does not rely on tensor-product construction of basis functions. Instead, it is constructed as a multilayered superposition of univariate synaptic functions and thus avoids the curse of dimensionality similarly to MLP, yet with improved local properties. Additionally, a fully deterministic initialization procedure based on principal component analysis is proposed for MS-FNN, in contrast to the usual random initialization of multilayer networks. Excellent performance of MS-FNN with one and two hidden layers, different activation functions, and B-splines of different orders is demonstrated for time series prediction and classification problems.


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Paper Citation

in Harvard Style

Kolodyazhniy V. (2011). MULTILAYER SPLINE-BASED FUZZY NEURAL NETWORK WITH DETERMINISTIC INITIALIZATION . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 475-481. DOI: 10.5220/0003652604750481

in Bibtex Style

author={Vitaliy Kolodyazhniy},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)
SN - 978-989-8425-83-6
AU - Kolodyazhniy V.
PY - 2011
SP - 475
EP - 481
DO - 10.5220/0003652604750481