Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function

Souza, Paulo Vitor de Campos; Torres, Luiz C. B.; Guimarães, Augusto Júnio; Araújo, Vanessa Souza; Araújo, Vinícius Jonathan Silva; Rezende, Thiago Silva

doi:10.1007/s00500-019-03792-z

Cited by 26 publications

(11 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Abubacker, et al [10] used fuzzy feed-forward back propagation neural network (ACFNN) for extending associative classifier. In mammography context, it is used as an effective classifier.…”

Section: Related Workmentioning

confidence: 99%

“…The diagnosis of RA in early stages are done by assisting the GPs by this tool as shown by the experimental results. Kourilovitch et al [10] used the parameters like involvement of joint, acute phase reactants level, serology and symptoms duration to construct a classification criteria. The patients with early RA are categorized using this classifier.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Critical Review on Medical Image Processing Techniques

2020

jcr

View full text Add to dashboard Cite

Medical image processing is highly a vital task in the field of health informatics. Various techniques are used to process the images captured using methods like Medical Resonance Imaging (MRI), Computed Tomography(CT), Positron Emission Tomography (PET) and the process includes, denoising, feature extraction, segmentation and classification. Different methods are implemented for every process. Process of eliminating noise from an acquired image is termed as denoising and various filters are designed for the same. Different features like local binary pattern, gray level similarity are extracted for segmentation process. Clustering algorithms like fuzzy-c-means, fuzzy-k-means algorithms are used for image segmentation. Particle Swarm Optimization (PSO), convolutional neural networks are also used for the purpose of image segmentation. Performance of these methods are compared using various performance evaluation metrics like, precision, recall, accuracy, sensitivity.

show abstract

“…Abubacker, et al [10] used fuzzy feed-forward back propagation neural network (ACFNN) for extending associative classifier. In mammography context, it is used as an effective classifier.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Critical Review on Medical Image Processing Techniques

2020

jcr

View full text Add to dashboard Cite

show abstract

“…The fuzzy neural network is a kind of special neural network that is a hybrid intelligent system formed by the combination of neural network and fuzzy logic. It combines the two kinds of techniques by combining the human-like reasoning of fuzzy systems with the learning and connection structure of neural networks [25,26]. In a nutshell, the fuzzy neural network (FNN) assigns a conventional neural network to fuzzy input signals and fuzzy weights.…”

Section: Sliding-mode Control Based On Complex Fuzzy Neural Networkmentioning

confidence: 99%

Picking Robot Visual Servo Control Based on Modified Fuzzy Neural Network Sliding Mode Algorithms

Chen

Liu

et al. 2019

Electronics

View full text Add to dashboard Cite

Through an analysis of the kinematics and dynamics relations between the target positioning of manipulator joint angles of an apple-picking robot, the sliding-mode control (SMC) method is introduced into robot servo control according to the characteristics of servo control. However, the biggest problem of the sliding-mode variable structure control is chattering, and the speed, inertia, acceleration, switching surface, and other factors are also considered when approaching the sliding die surface. Meanwhile, neural network has the characteristics of approaching non-linear function and not depending on the mechanism model of the system. Therefore, the fuzzy neural network control algorithm can effectively solve the chattering problem caused by the variable structure of the sliding mode and improve the dynamic and static performances of the control system. The comparison experiment is carried out through the application of the PID algorithm, the sliding mode control algorithm, and the improved fuzzy neural network sliding mode control algorithm on the picking robot system in the laboratory environment. The result verified that the intelligent algorithm can reduce the complexity of parameter adjustments and improve the control accuracy to a certain extent.

show abstract

“…Finally, in the last decade, scientific work using such techniques has addressed detection of celestial materials (called Pulsars) [63]; prediction of autism in children [64], adolescents [65], and adults [66] through database obtained from mobile devices; breast cancer [67]; absenteeism at work [68]; control for active power filter [69,70]; data knowledge [71]; and speech recognition [72]. The diversity of these models also covers the field of predicting software building efforts [73], cybersecurity [74], help in cryotherapy and immunotherapy treatments [75,76], and different classification and regression problems [77][78][79][80][81][82][83], where the models differ according to the training techniques, fuzzification or defuzzification process, architecture, number of layers, elements present in the model structure, etc.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Fuzzy Rules to Help Predict Rains and Temperatures in a Brazilian Capital State Based on Data Collected from Satellites

2019

Self Cite

View full text Add to dashboard Cite

Featured Application: A hybrid model formed by neural network techniques and fuzzy systems that predicts rainfall and temperatures in the capital of Minas Gerais, Brazil, which contains several river sources essential for the Brazilian economy. The purpose of the paper is to construct a model capable of extracting knowledge from satellite data related to temperatures and rainfall forecast of the analyzed state.Abstract: The forecast for rainfall and temperatures in underdevelope countries can help in the definition of public and private investment strategies in preventive and corrective nature. Water is an essential element for the economy and living things. This study had a main objective to use an intelligent hybrid model capable of extracting fuzzy rules from a historical series of temperatures and rainfall indices of the state of Minas Gerais in Brazil, more specifically in the capital. Because this is state has several rivers fundamental to the Brazilian economy, this study intended to find knowledge in the data of the problem to help public managers and private investors to act dynamically in the prediction of future temperatures and how they can interfere in the decisions related to the population of the state. The results confirm that the intelligent hybrid model can act with efficiency in the generation of predictions about the temperatures and average rainfall indices, being an efficient tool to predict the water situation in the future of this critical state for Brazil. Appl. Sci. 2019, 9, 5476 2 of 30 watersheds critical nationally. Along with several great cities, it has suffered a strong impact from drought [4].In contrast to the lack of water, it has problems with excess: floods and landslides [2]. If a river or stream receives a considerable amount of water from rain and cannot support it, it ends up overflowing and causing floods, which bring with it destruction, damage, and even death.The facts cited above are just some of the impacts that rain has on society. Within this context, this work was developed for the use of neural networks in the forecast of volumetric rainfall, based on a fuzzy rule system, developing a precise forecast, and contributing to the anticipation of possible tragedies s in places that occur phenomena of greater magnitude, as well as helping in a better control of agriculture. Another purpose was to support air and sea transportation, avoiding accidents in their trajectories. Briefly, when anticipating a possible problem, it is also possible to anticipate solutions and reduce their impacts, such as evacuating areas at risk of landslides or flooding [5].Currently, many kinds of research are being developed to apply models of artificial neural networks in rain forecasts, river basins, and water studies. For example, it was applied to the problem of forecasting the flow of the Nile River in Egypt [6], using a temporal series as a reference to compare tests among several prediction methods of neural networks. Besides, there was a study using stochastic autoregressive mean m...

show abstract

Data density-based clustering for regularized fuzzy neural networks based on nullneurons and robust activation function

Cited by 26 publications

References 40 publications

A Critical Review on Medical Image Processing Techniques

A Critical Review on Medical Image Processing Techniques

Picking Robot Visual Servo Control Based on Modified Fuzzy Neural Network Sliding Mode Algorithms

Fuzzy Rules to Help Predict Rains and Temperatures in a Brazilian Capital State Based on Data Collected from Satellites

Contact Info

Product

Resources

About