The Bounded Capacity of Fuzzy Neural Networks (FNNs) Via a New Fully Connected Neural Fuzzy Inference System (F-CONFIS) With Its Applications

Wang, Jing; Wang, Chi-Hsu; Chen, C. L. Philip

doi:10.1109/tfuzz.2013.2292972

Cited by 19 publications

(6 citation statements)

References 48 publications

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“…A FNIS has the characteristics of fuzzy logic in dealing with uncertainty and the ability to learn and generalize the knowledge learned from an ANN MLP type. Such a system follows the universal approximation theorem, i.e., it can correctly approximate nonlinear functions (Wang, et al, 2014). This system is characterized by being a FIS, the inputs and outputs of which are crisp numbers.…”

Section: Artificial Neural Network and Fuzzy Logicmentioning

confidence: 99%

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Fuzzy Inference System and Fuzzy Neural Inference System Applied to Risk Matrix Classification in Projects

Gatto

Leonardis

Napolitano

et al. 2023

CLIUM

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Projects are essential for organizations to transform strategies into results, but uncertain events can impose risks to achieve a certain objective. Risk management aims to support an organization in deciding how to deal with risks, prioritizing them through the application of Risk Matrices (RMs). RMs or Probability and Impact Matrices is used to support decision-making, helping management to classify and prioritize risks to decide which will be ad-dressed, monitored, or tolerated. RMs are supposedly easy to build and explain, but according to the literature they may contain uncertainties. To deal with uncertainty, it is recommended to apply a Fuzzy Inference System, based on Fuzzy Set Theory (FST) or a Fuzzy Neural Inference System with the presence of an artificial neural network. Thus, the aim of this paper was to develop and apply a Fuzzy Inference System (FIS) and a Fuzzy Neural Inference System (FNIS) in the classification of MRs in projects to reduce uncertainty. The analysis of the results indicated that the application of the two systems resulted in a continuous classification rule by smoothing the boundary areas between each of the RM classes, reducing uncertainty and improving risk classification. Both systems showed good results in reducing uncertainty. However, the results obtained with FNIS were more consistent. The main contribution of this work lies in the possibility of improving the decision making by reducing the uncertainty present in RMs.

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Section: Artificial Neural Network and Fuzzy Logicmentioning

confidence: 99%

“…ANNs and FL can be associated, generating a Fuzzy Neural Inference System (FNIS), including in the same model the treatment of the uncertainty of FL with the ability to learn and generalize the knowledge learned from an ANN (Wang, et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Inference System and Fuzzy Neural Inference System Applied to Risk Matrix Classification in Projects

Gatto

Leonardis

Napolitano

et al. 2023

CLIUM

View full text Add to dashboard Cite

show abstract

“…Common classifiers include tree structure classifiers [36,37], neural network classifiers [38,39], and support vector machine classifiers [40,41]. The tree structure classifier used a multilevel classifier structure, each structure according to one or more of the characteristic parameters.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Multilayer Learning Network for Modulation Classification Assisted with Frequency Offset Cancellation in Satellite to Ground Link

Yang

2018

Wireless Communications and Mobile Computing

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A multilayer learning network assisted with frequency offset cancellation is proposed for modulation classification in satellite to ground link. Carrier frequency offset greatly reduces modulation classification performance. It is necessary to cancel frequency offset before modulation classification. Frequency offset cancellation weights are established through multilayer learning network based on MSE criterion. Then the weight and hidden layer of multilayer learning network are also established for modulation classification. The hidden layers and weight are trained and tuned to combat the interference introduced by frequency offset. Compared with current modulation classification algorithm, the proposed multilayer learning network greatly improves the Probability of Correct Classification (PCC). It has been proven that the proposed multilayer learning network assisted with frequency offset has higher performance for modulation classification within the same training sequence.

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“…Varying criteria have been proposed to define the upper and lower bound of the number of hidden neurons. In this study, the criterion proposed by Wang et al [65] is applied to determine the suitable bounds of the number of hidden neurons used in the feedforward networks…”

Section: Building the Prediction Modelmentioning

confidence: 99%

Smart Information Reconstruction via Time-Space-Spectrum Continuum for Cloud Removal in Satellite Images

Chang

Bai

Chen

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Cloud contamination is a big obstacle when processing satellite images retrieved from visible and infrared spectral ranges for application. Although computational techniques including interpolation and substitution have been applied to recover missing information caused by cloud contamination, these algorithms are subject to many limitations. In this paper, a novel smart information reconstruction (SMIR) method is proposed, in order to reconstruct cloud contaminated pixel values from the time-space-spectrum continuum with the aid of a machine learning tool, namely extreme learning machine (ELM). For the purpose of demonstration, the performance of SMIR is evaluated by reconstructing the missing remote sensing reflectance values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite over Lake Nicaragua, where is a very cloudy area year round. For comparison, the traditional backpropagation neural network algorithms will also be implemented to reconstruct the missing values. Experimental results show that the ELM outperforms the BP algorithms by an enhanced machine learning capacity with simulated memory effect embedded in MODIS due to linking the complex time-space-spectrum continuum between cloud-free and cloudy pixels. The ELM-based SMIR practice presents a correlation coefficient of 0.88 with root mean squared error of 7.4E − 04sr −1 between simulated and observed reflectance values. Finding suggests that the SMIR method is effective to reconstruct all the missing information providing visually logical and quantitatively assured images for further image processing and interpretation in environmental applications.Index Terms-Artificial neural network, cloud removal, computational intelligence, extreme learning machine, machine learning, satellite images.

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The Bounded Capacity of Fuzzy Neural Networks (FNNs) Via a New Fully Connected Neural Fuzzy Inference System (F-CONFIS) With Its Applications

Cited by 19 publications

References 48 publications

Fuzzy Inference System and Fuzzy Neural Inference System Applied to Risk Matrix Classification in Projects

Fuzzy Inference System and Fuzzy Neural Inference System Applied to Risk Matrix Classification in Projects

Multilayer Learning Network for Modulation Classification Assisted with Frequency Offset Cancellation in Satellite to Ground Link

Smart Information Reconstruction via Time-Space-Spectrum Continuum for Cloud Removal in Satellite Images

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