Local greedy approximation for nonlinear regression and neural network training

Verification of an Evolutionary-based Wavelet Neural Network Model for Nonlinear Function Approximation

Nonlinear function approximation is one of the most important tasks in system analysis and identification. Several models have been presented to achieve an accurate approximation on nonlinear mathematics functions. However, the majority of the models are specific to certain problems and systems. In this paper, an evolutionary-based wavelet neural network model is proposed for structure definiti...

A Differential Evolution and Spatial Distribution based Local Search for Training Fuzzy Wavelet Neural Network

Abstract   Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...

Regularized Greedy Algorithms for Neural Network Training with Data Noise

The aim of this paper is to construct a modified greedy algorithm applicable for an ill-posed function approximation problem in presence of data noise. This algorithm, coupled with a suitable stopping rule, can be interpreted as an iterative regularization method. We provide a detailed convergence analysis of the algorithm in presence of noise, and discuss optimal choices of parameters. As a co...

Some Greedy Algorithms for Sparse Nonlinear Regression

Nonlinear Survival Regression Using Artificial Neural Network

Survival analysismethods deal with a type of data, which is waiting time till occurrence of an event. One commonmethod to analyze this sort of data is Cox regression. Sometimes, the underlying assumptions of the model are not true, such as nonproportionality for the Cox model. In model building, choosing an appropriate model depends on complexity and the characteristics of the data that effect ...