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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...
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...
Nowadays, artificial intelligence has a wide usage especially for designing intelligent systems in medicine. Diagnosing and determining different kinds of diseases are a part of this system's duties. In this research tried to diagnose liver disorders more accurate by using Hopfield neural network and fuzzy Hopfield beside fuzzy C-Means. Requiring data including 345 records and 6 fields is chosen from...
The failures auto-sensing becomes increasingly essential in the complex systems exploitation. This article consists in working out a system of defects diagnosis based on an artificial intelligence technique which associates fuzzy logic with neural networks. The method is applied to obtain the DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems). This...
An electronic nose system had been developed by using 16 quartz resonator sensitive membranes-basic resonance frequencies 20 MHz as a sensor, and analyzed the measurement data through various neural network as a pattern recognition system. The developed system showed high recognition probability to discriminate various single odors even mixture odor to its high generality properties; however the system...
Type 2 fuzzy systems have been under investigation for a while and the projection of type 2 understanding for uncertainty management onto the connectionist models -i.e. neural networks- seems an interesting field of research. This paper considers neurons having multiple bias values defining a new structure that resembles the uncertainty handling capability of type 2 fuzzy models. Such a neuron provides...
Many practical problems are characterized as decision making with multiple, conflicting and noncommensurable nonlinear objectives and complex criteria. Especially in the practice of purchasing decision making, many quantitative and qualitative factors must be considered, as well as the vagueness and imprecision among them, which makes the decision process more complicated and unstructured. For identifying...
For its sensitive dependence with the initial value, chaos can be used to the pattern recognition of the ones with extremely small difference. An algorithm based on chaotic fuzzy RBF neural network (CFRBF) is proposed and used for face recognition. For introducing chaotic noise, the network obtains a better anti-jamming. It can avoid being affected by the factors such as illumination and gesture....
This paper presents a simulation of Neuro-Fuzzy application for analysing studentspsila performance based on their CPA and GPA. The analysis is an extension of our previous study, which was called an analysis on studentpsilas performance using fuzzy systems. The main function of this analysis is to support the development of intelligent planning system (INPLANS) using fuzzy systems, neural networks,...
In this paper, we propose a fast C-means based training of Fuzzy Hopfield neural network and apply it to image segmentation. According to the other ways which usually take a long time, we define a fast method for image segmentation. We present a new objective function, and its minimization by Lyapunov energy function which is based on two dimensional fuzzy Hopfield neural network. This objective function...
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