Type-2 fuzzy wavelet networks (T2FWN) for system identification using fuzzy differential and Lyapunov stability algorithm

Singh, Madhusudan; Srivastava, Smriti; Hanmandlu, M.; Gupta, J. R. P.

doi:10.1016/j.asoc.2008.03.017

Cited by 35 publications

(22 citation statements)

References 23 publications

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“…Most of the method basing on fuzzy logic adopt Type-1 fuzzy sets representing uncertainties with the range between [0,1] and type-1 fuzzy sets is having a precise membership function where its elements are real number. To handle [28] this difficulties a type-2 fuzzy sets is introduced which is most able to handle the uncertainty related to noisy and non-stationary than type-1 fuzzy set along with allowing uncertainty [29][30][31][32] to its associated membership degree. For the prediction of stock price Chih-Feng et al presented a type-2 neuro-fuzzy model where [28] a self constructed clustering method designed the type-2 fuzzy rules and then refined it by a hybrid algorithm.…”

Section: Psomentioning

confidence: 99%

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Das¹,

Mishra²,

Rout³

2017

JESTR

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Financial forecasting is one of the imperative fields of research, where investors invest money and get restless for the future changes of the stock values in the market. In the recent course of time forecasting stock price is one of the challenging tasks. To predict the stock price most Artificial Neural Network (ANN) based model are used in the historical data along with statistical measures, technical indicators etc. With the development of ANNs, investors are hoping the best prediction because networks have great capability of machine learning problems such as classification and prediction. ANNs particularly Back Propagation (BP) has overlooked the non-stationary and noise characteristics of stock market data, as the training of BP is intricate due to the noise data and the network fall into a natural solution such as always predict an usual output. Most optimization techniques have been used for training the weights of forecasting models. Since no single optimization technique is invariably superior to others. Recently various nature inspired optimization techniques have been introduced and successfully employed to many fields of Financial Engineering. This survey aims to study the mostly used Bio-inspired optimization techniques on Stock Market Prediction and makes a comparative analysis between them.

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Section: Psomentioning

confidence: 99%

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Das¹,

Mishra²,

Rout³

2017

JESTR

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“…Conventionally, design factors are determined for once for the whole rule base in the previous papers [16,18,25,36]…”

Section: Type-2 Fuzzy Multiplication Wavelet Neural Network Modelmentioning

confidence: 99%

“…However, type-2 fuzzy logic systems give better results in many areas. They have been applied to many different applications such as identification of nonlinear systems [16][17][18][19][20][21], control [22,23], time series prediction [24], system modeling [20,25,26], stock price prediction [27] and control of mobile robots [28,29]. In [30], a review of type-2 fuzzy logic applications is presented for pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Multiplication Wavelet Neural Network Model Design for Classification of Breast Cancer

Gündüz

2018

ijarcs

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Breast cancer is one of the most common types of cancer. For this reason, it is very important to diagnose breast cancer. In this paper, a type-2 fuzzy multiplication wavelet neural network model is proposed to classify the Wisconsin Breast Cancer dataset. In this model, Shannon wavelet function is used as the type-2 membership function and the multiplication of the Shannon wavelet functions is used in the conclusion part of the rules. The results of proposed model is compared with type-1 fuzzy multiplication wavelet neural network, multilayer perceptron network, radial basis function network, Bayesian network learning, and decision tree algorithm. It can be seen that proposed type-2 fuzzy multiplication wavelet neural network model gives the best results among these algorithms.

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“…[44]. Proposed IT2-NTSK-FNN is composed of seven layers that in first two layers an interval type-2 fuzzy neuron is used for fuzzifing.…”

Section: Type-2 Fuzzy Logic and Systemsmentioning

confidence: 99%

Adaptive Intelligent Inverse Control Of Nonlinear Systems With Regard To Sensor Noise And Parameter Uncertainty (Magnetic Ball Levitation System Case Study)

Asad

Shamsi

Ivani

et al. 2016

International Journal on Smart Sensing and Intelligent Systems

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Type-2 fuzzy wavelet networks (T2FWN) for system identification using fuzzy differential and Lyapunov stability algorithm

Cited by 35 publications

References 23 publications

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Type-2 Fuzzy Multiplication Wavelet Neural Network Model Design for Classification of Breast Cancer

Adaptive Intelligent Inverse Control Of Nonlinear Systems With Regard To Sensor Noise And Parameter Uncertainty (Magnetic Ball Levitation System Case Study)

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