A Complex Texture Classification Algorithm based on Gabor-type Filtering Cellular Neural Networks and Self-Organized Fuzzy Inference Neural Networks

A Cellular Neural Networks (CNN)-based nonlinear pattern classification algorithm utilizing the most likely path-finding feature of dynamic programming is proposed. Dynamic programming for the most likely path-finding algorithm can be implemented with CNN. If exemplars and test patterns are assigned as the goals and the start positions, respectively for our CNN-based dynamic programming, the paths from the test patterns to their closest exemplars are found with the optimality feature of CNN-based dynamic programming. Such paths are utilized as aggregating keys for classification. Our algorithm is suitable for patterns with nonlinear pattern boundaries. Simulation results are included.

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Sensor encoding using lateral inhibited self-organized cellular neural networks

Neural Networks ◽

10.1016/0893-6080(95)00038-0 ◽

1996 ◽

Vol 9 (1) ◽

pp. 99-120 ◽

Cited By ~ 3

Author(s):

Rüdiger W. Brause

Keyword(s):

Neural Networks ◽

Cellular Neural Networks ◽

Self Organized

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Efficient algorithms for space image processing and their realization in cellular neural networks

Kosmìčna nauka ì tehnologìâ ◽

10.15407/knit1998.04.074 ◽

1998 ◽

Vol 4 (4) ◽

pp. 74-84

Author(s):

I.N. Aizenberg ◽

Keyword(s):

Neural Networks ◽

Image Processing ◽

Cellular Neural Networks ◽

Efficient Algorithms ◽

Space Image

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FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v8i3.3399 ◽

2009 ◽

Vol 8 (3) ◽

pp. 887-897

Author(s):

Vishal Paika ◽

Er. Pankaj Bhambri

Keyword(s):

Neural Networks ◽

Fuzzy Inference ◽

Facial Features ◽

Facial Appearance ◽

Feature Points ◽

Inference System ◽

Detection Techniques ◽

The Face ◽

Hair Style ◽

Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

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Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

Journal of Thermoplastic Composite Materials ◽

10.1177/08927057211013070 ◽

2021 ◽

pp. 089270572110130

Author(s):

Gökçe Özden ◽

Mustafa Özgür Öteyaka ◽

Francisco Mata Cabrera

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Cutting Forces ◽

Fuzzy Inference ◽

Test Results ◽

Inference System ◽

Good Prediction Accuracy ◽

Peek Composite ◽

Artificial Neural ◽

Neural Fuzzy

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination ( R2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.

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