Initialization by selection for wavelet network training
نویسندگان
چکیده
منابع مشابه
Initialization by selection for wavelet network training
We present an original initialization procedure for the parameters of feedforward wavelet networks, prior to training by gradient-based techniques. It takes advantage of wavelet frames stemming from the discrete wavelet transform, and uses a selection method to determine a set of best wavelets whose centers and dilation parameters are used as initial values for subsequent training. Results obta...
متن کاملInitialization by selection for multi library wavelet neural network training
This paper presents an original architecture of Wavelet Neural Network based on multi Wavelets activation function and uses a selection method to determine a set of best wavelets whose centers and dilation parameters are used as initial values for subsequent training library WNN for one dimension and two dimensions function approximation. Every input vector will be considered as unknown functio...
متن کاملInitialization by a Novel Clustering for Wavelet Neural Network as Time Series Predictor
The architecture and parameter initialization of wavelet neural network are discussed and a novel initialization method is proposed. The new approach can be regarded as a dynamic clustering procedure which will derive the neuron number as well as the initial value of translation and dilation parameters according to the input patterns and the activating wavelets functions. Three simulation examp...
متن کاملA memetic GSA with niching selection for training fuzzy wavelet neural network
This paper proposes an effective memetic Gravitational Search Algorithm (GSA) that utilizes Solis and Wets’ (SW) algorithm as local search. GSA has good exploration ability and SW helps to improve the exploitation ability of the memetic algorithm. Furthermore, a selection strategy is proposed to select suitable individuals for local refinement that is based on subtractive clustering. Proposed m...
متن کاملA 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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2000
ISSN: 0925-2312
DOI: 10.1016/s0925-2312(00)00295-2