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Design of an Electro-Hydraulic System Using Neuro-Fuzzy Techniques  [PDF]
P. J. Costa Branco,J. A. Dente
Computer Science , 2000,
Abstract: Increasing demands in performance and quality make drive systems fundamental parts in the progressive automation of industrial processes. Their conventional models become inappropriate and have limited scope if one requires a precise and fast performance. So, it is important to incorporate learning capabilities into drive systems in such a way that they improve their accuracy in realtime, becoming more autonomous agents with some degree of intelligence. To investigate this challenge, this chapter presents the development of a learning control system that uses neuro-fuzzy techniques in the design of a tracking controller to an experimental electro-hydraulic actuator. We begin the chapter by presenting the neuro-fuzzy modeling process of the actuator. This part surveys the learning algorithm, describes the laboratorial system, and presents the modeling steps as the choice of actuator representative variables, the acquisition of training and testing data sets, and the acquisition of the neuro-fuzzy inverse-model of the actuator. In the second part of the chapter, we use the extracted neuro-fuzzy model and its learning capabilities to design the actuator position controller based on the feedback-error-learning technique. Through a set of experimental results, we show the generalization properties of the controller, its learning capability in actualizing in realtime the initial neuro-fuzzy inverse-model, and its compensation action improving the electro-hydraulics tracking performance.
Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications  [PDF]
D. Srinivasa Rao,M. Seetha,M. H. M. Krishna Prasad
Computer Science , 2012, DOI: 10.5120/6222-8800
Abstract: Image fusion is the process of integrating multiple images of the same scene into a single fused image to reduce uncertainty and minimizing redundancy while extracting all the useful information from the source images. Image fusion process is required for different applications like medical imaging, remote sensing, medical imaging, machine vision, biometrics and military applications where quality and critical information is required. In this paper, image fusion using fuzzy and neuro fuzzy logic approaches utilized to fuse images from different sensors, in order to enhance visualization. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices for image fusion like image quality index, mutual information measure, fusion factor, fusion symmetry, fusion index, root mean square error, peak signal to noise ratio, entropy, correlation coefficient and spatial frequency. Experimental results obtained from fusion process prove that the use of the neuro fuzzy based image fusion approach shows better performance in first two test cases while in the third test case fuzzy based image fusion technique gives better results.
Neuro Fuzzy based Techniques for Predicting Stock Trends  [PDF]
Hemanth Kumar P.,Prashanth K B,Nirmala T V,S.Basavaraj Patil
International Journal of Computer Science Issues , 2012,
Abstract: In this paper we discuss about Prediction of stock market returns.Artificial neural networks (ANNs) have been popularly applied to finance problems such as stock exchange index prediction, bankruptcy prediction and corporate bond classification. An ANN model essentially mimics the learning capability of the human brain. A Fuzzy system can uniformly approximate any real continuous function on a compact domain to any degree of accuracy. HereNeuro Fuzzy approaches for predicting financial time series are investigated and shown to perform well in the context of various trading strategies involving stocks. The horizon of prediction is typically a few days and trading strategies are examined using historical data. Methodologies are presented wherein neural predictors are used to anticipate the general behavior of financial indexes in the context of stocks and options trading. The methodologies are tested with actual financial data and shown considerable promise as a decision making and planning tool. In this paper methods are designed to predict 10-15 days of stock returns in advance.
Characterization of Tumor Region Using SOM and Neuro Fuzzy Techniques in Digital Mammography  [PDF]
Anamika Ahirwar,R.S. Jadon
International Journal of Computer Science & Information Technology , 2011,
Abstract: Nowadays the most common type of cancer in women is breast cancer. This is the second main cause of cancer deaths in women. Digital mammography is the technique which is used to examine the breast.This is very much useful for the detection of breast diseases in women. The automatic detection of tumor or some type of deformity in the medical imaging is done by many researchers to develop somealgorithms and methods. In this paper we are using SOM and Fuzzy c-means clustering techniques for tumor detection in digital mammography images. We then further calculate the statistical features of tumor like location of tumor, area, energy, entropy, idm, mean, contrast, mean and standard deviation which helps the radiologist to study the statistical information regarding breast cancer, so that the doctors can give better treatment to patients. For calculating these statistical properties we use regiongrowing and region merging techniques.
A Multitarget Tracking Video System Based on Fuzzy and Neuro-Fuzzy Techniques  [cached]
Jesús García,José M. Molina,Juan A. Besada,Javier I. Portillo
EURASIP Journal on Advances in Signal Processing , 2005, DOI: 10.1155/asp.2005.2341
Abstract: Automatic surveillance of airport surface is one of the core components of advanced surface movement, guidance, and control systems (A-SMGCS). This function is in charge of the automatic detection, identification, and tracking of all interesting targets (aircraft and relevant ground vehicles) in the airport movement area. This paper presents a novel approach for object tracking based on sequences of video images. A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets from the image regions extracted in the images. The advantages of this approach are robustness, flexibility in the design to adapt to different situations, and efficiency for operation in real time, avoiding combinatorial enumeration. Results obtained in representative ground operations show the system capabilities to solve complex scenarios and improve tracking accuracy. Finally, an automatic procedure, based on neuro-fuzzy techniques, has been applied in order to obtain a set of rules from representative examples. Validation of learned system shows the capability to learn the suitable tracker decisions.
Performance Analysis of Optical Wireless Communication System Employing Neuro-Fuzzy Based Spot-Diffusing Techniques  [PDF]
Shamim Al Mamun, M. Shamim Kaiser, Muhammad R Ahmed, Md. Shafiqul Islam, Md. Imdadul Islam
Communications and Network (CN) , 2013, DOI: 10.4236/cn.2013.53B2048
Abstract: The spot-diffusing technique provides better performance compared to conventional diffuse system for indoor optical-wireless communication (OWC) system. In this paper, the performance of an OW spot-diffusing communication system using Neuro-Fuzzy (NF) adaptive multi-beam transmitter configuration has been proposed. The multi-beam transmitter generates multiple spots pointed in different directions, hence, forming a matrix of diffusing spots based on position of the receiver and receiver mobility. Regardless of the position of the transmitter and receiver, NF controller target the spots adaptively at the best locations and allocates optimal power to the spots and beam angle are adapted in order to achieve better signal-to-noise plus interference ratio (SNIR). Maximum ratio combining (MRC) is used in the imaging receiver. The proposed OW spot-diffusing communication system is compared with other spot-beam diffusion methods proposed in literature. Performance evaluation revels that the proposed NF based OW spot-diffusing communication system outperforms other spot-beam diffusion methods.
Fuzzy Logic and Neuro-fuzzy Systems: A Systematic Introduction
Yue Wu,Biaobiao Zhang,Jiabin Lu,K. -L. Du
International Journal of Artificial Intelligence and Expert Systems , 2011,
Abstract: Fuzzy logic is a rigorous mathematical field, and it provides an effective vehicle for modeling the uncertainty in human reasoning. In fuzzy logic, the knowledge of experts is modeled by linguistic rules represented in the form of IF-THEN logic. Like neural network models such as the multilayer perceptron (MLP) and the radial basis function network (RBFN), some fuzzy inference systems (FISs) have the capability of universal approximation. Fuzzy logic can be used in most areas where neural networks are applicable. In this paper, we first give an introduction to fuzzy sets and logic. We then make a comparison between FISs and some neural network models. Rule extraction from trained neural networks or numerical data is then described. We finally introduce the synergy of neural and fuzzy systems, and describe some neuro-fuzzy models as well. Some circuits implementations of neuro-fuzzy systems are also introduced. Examples are given to illustrate the concepts of neuro-fuzzy systems.
Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques  [cached]
G. Yang,Y. Lin,P. Bhattacharya
EURASIP Journal on Advances in Signal Processing , 2007, DOI: 10.1155/2008/371621
Abstract: To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i) casual or contextual feature, (ii) contact feature, (iii) contactless feature, and (iv) performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK) model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA), is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue). We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach  [PDF]
Akhilesh K Verma,Soumi Chaki,Aurobinda Routray,William K Mohanty,Mamata Jenamani
Computer Science , 2015, DOI: 10.1016/j.jappgeo.2014.10.005
Abstract: In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.
Efficient Cancer Classification using Fast Adaptive Neuro-Fuzzy Inference System (FANFIS) based on Statistical Techniques  [PDF]
K.Ananda Kumar,Dr.M.Punithavalli
International Journal of Advanced Computer Sciences and Applications , 2011,
Abstract: The increase in number of cancer is detected throughout the world. This leads to the requirement of developing a new technique which can detect the occurrence the cancer. This will help in better diagnosis in order to reduce the cancer patients. This paper aim at finding the smallest set of genes that can ensure highly accurate classification of cancer from micro array data by using supervised machine learning algorithms. The significance of finding the minimum subset is three fold: a) The computational burden and noise arising from irrelevant genes are much reduced; b) the cost for cancer testing is reduced significantly as it simplifies the gene expression tests to include only a very small number of genes rather than thousands of genes; c) it calls for more investigation into the probable biological relationship between these small numbers of genes and cancer development and treatment. The proposed method involves two steps. In the first step, some important genes are chosen with the help of Analysis of Variance (ANOVA) ranking scheme. In the second step, the classification capability is tested for all simple combinations of those important genes using a better classifier. The proposed method uses Fast Adaptive Neuro-Fuzzy Inference System (FANFIS) as a classification model. This classification model uses Modified Levenberg-Marquardt algorithm for learning phase. The experimental results suggest that the proposed method results in better accuracy and also it takes lesser time for classification when compared to the conventional techniques.
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