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Artificial Neural networks are utilized to predict flow properties of a confined, isothermal, and swirling flowfield in an axisymmetric sudden expansion combustor using a two-component laser Doppler velocimetry capable of measuring the mean velocity components and their statistics. Generalized feedforward, radial basis function, and coactive neuro-fuzzy inference system neural networks are tested...
Fuzzy neural networks are hybrid models capable to approximate functions with high precision and to generate transparent models, enabling the extraction of valuable information from the resulting topology. In this paper we will show that the recently proposed fuzzy neural network based on weighted uninorms aggregations uniformly approximates any real functions on any compact set. We will describe...
Knowledge manufacturing system has the ability of modifying dynamically manufacturing mode rapidly when production environment factors change. It is essential to evaluate the matching degree of established manufacturing mode and changed production environment factors. In this paper, a matching decision model for self-adaptability of knowledge manufacturing system based on the fuzzy neural network...
In this paper, we present a new learning algorithm for self-constructing fuzzy neural networks (FNN). First, an initial network starts with no hidden neurons and grows neurons based on the growth criteria. After the generation process, a neuron pruning algorithm based on optimal brain surgeon (OBS) is employed to reduce the size of the FNN. After the structure design process, weight adjustment method...
The risk assessment is an important means to obtain the status of system security and an important basis to develop security solutions as well. Taking the integrated navigation system as the research object, this article proposes an evaluation model which can apply to the system of risk assessment taken by the neural network and the fuzzy theory. The model use the fuzzy evaluation method to quantize...
This paper presents an intelligent control approach for blood pressure system using self-generating fuzzy neural networks (SGFNN). The proposed SGFNN is simple and effective and is able to generate a fuzzy neural network to model unknown nonlinearities of complex blood pressure system. This paper investigates the use of fuzzy neural network technique for modeling and automatic control of mean arterial...
The prediction of exploitable reserves of oil layer is a complicated problem, which involves many geological and crude oil parameters. Considering its intrinsic properties, this paper put forward an improved fuzzy neural network (FFN) method, and compared it with the traditional BP method. The results showed that this method has better accuracy and reliability, hence it may provide an important reference...
The most important challenge in Wireless Sensor Networks (WSNs) is to improve the operational efficiency in highly resource constrained environment based on dynamic and unpredictable behaviour of network parameters and applications requirement. In this paper we have proposed a method for clustering and their analysis to study the cluster formation, their behaviour with respect to the system parameters...
The primary objective of steelmaking through Basic Oxygen Furnace (BOF) process is to achieve desired end point carbon content, temperature and percentage composition at the lowest cost and in the shortest possible time. As of now, most widely used models for prediction of parameters of converter steelmaking are mechanistic model, statistical model and neural network model for the prediction of the...
In this paper the robustness of three different types of Fuzzy Flip-Flop based Neural Network (FNN) and the standard tansig based neural networks is compared from the various test function approximation goodness points of view. It is tested how well the fuzzy flip-flop based and the simulated neural networks handle the test data sets outlier points. The robust design of the FNN is presented, and the...
Due to the advent of computer technology image-processing techniques have become increasingly important in a wide variety of applications. This is particularly true for medical imaging such as Computer Tomography (CT), magnetic resonance image (MRI), and nuclear medicine, which can be used to assist doctors in diagnosis, treatment, and research. In this paper, hybrid algorithm for segmentation of...
This paper presents an efficient method for Persian signature recognition based on Fuzzy RBF neural network (FRBF). A new training method will be presented which had a very low error rates in Persian signature recognition. In this training algorithm, connection weights, centers, width and number of RBF units will be determined during training phase. FCM algorithm will be used for initializing parameters...
For the speckle consisted in Synthetic Aperture Radar (SAR) image,good result of SAR image segmentation can not be gotten with traditional methods. In this paper, a new method of SAR image segmentation is proposed with the combination of fuzzy neural network and the statistical feature of SAR image. Firstly, texture features of SAR image are extracted with gray level co-occurrence matrix, then SAR...
A new training algorithm for hierachical hybrid fuzzy - neural network (HHFNN) based on Takagi - Sugeno (T-S) fuzzy system is proposed in this paper. Triangular membership function is adopted. And to reduce the strong interaction among discrete input variables, coefficient contraction method is employed; ridge regression function is used in the THEN parts of fuzzy rules. At last, pyrimidines medical...
In order to resolve the computational complexity for local map matching of hierarchical simultaneous localization and mapping (SLAM), a novel self-organizing fuzzy neural networks (SOFNN) based approach was proposed in this paper. The matching component for local maps in the hierarchical SLAM is realized by an SOFNN. A subset of signature elements included in a local map was chosen by a clustering...
Takagi-Sugeno (T-S) fuzzy system was merged into Hierarchical Hybrid Fuzzy-Neural Networks (HHFNN) and homogeneous linear function of input variables was employed in the THEN part of fuzzy rules of T-S fuzzy systems. A new training algorithm for this model was also proposed. The parameters consist of the coefficients of homogeneous linear functions and the weights and bias terms of upper neural network...
A blind equalization algorithm based on fuzzy neural network was proposed. Blind channel estimation and fuzzy neural network classifier were utilized to realize blind equalization. Firstly Blind channel estimation was used to identify the character of the channel. Signals were rebuilt by de-convolution, and the original judgment equipment was replaced by fuzzy neural network classifier. Simulations...
A recursive extension of Gath-Geva clustering algorithm is proposed in this paper which is used as a basis for online tuning and development of neuro-fuzzy models. In comparison with other online modeling approaches which use spherical clusters for defining validity region of neurons, the proposed evolving neuro-fuzzy model (ENFM) has the ability to take advantage of elliptical clusters. This extension...
This paper proposes a method for the identification of evolving fuzzy Takagi-Sugeno systems based on the Optimally-Pruned Extreme Learning Machine (OP-ELM) methodology. We describe ELM which is a simple yet accurate and fast learning algorithm for training single-hidden layer feed-forward artificial neural networks (SLFNs) with random hidden neurons. We then describe the OP-ELM methodology for building...
A new triangular t-norm and t-conorm are presented. The new fuzzy operations combined with the standard negation are applied in a practical problem, namely, they are proposed as suitable triangular norms for defining a fuzzy flip-flop based neuron. Other fuzzy J-K and D flip-flop based neurons are constructed by using algebraic, Łukasiewicz, Yager, Dombi and Hamacher connectives. The function approximation...
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