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Hybrid Learning Algorithm (hybrid + learning_algorithm)

Distribution by Scientific Domains

Engineering	100%

Selected Abstracts

Crack identification of a planar frame structure based on a synthetic artificial intelligence technique

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 1 2003
Mun-Bo Shim
Abstract It has been established that a crack has an important effect on the dynamic behaviour of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a planar frame structure, a method is presented in this paper which uses a synthetic artificial intelligence technique, i.e. adaptive-network-based fuzzy inference system (ANFIS) solved via a hybrid learning algorithm (the backpropagation gradient descent and the least-squares method) and continuous evolutionary algorithms (CEAs) solving single objective optimization problems with a continuous function and continuous search space efficiently are unified. With ANFIS and CEAs it is possible to formulate the inverse problem. ANFIS is used to obtain the input (the location and depth of a crack),output (the structural eigenfrequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this idea on 2D beam structures and the results are promising. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Anfis models for the quasistatic analysis of coplanar strip line structures

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 9 2010
Mustafa Turkmen
Abstract In this work, computer-aided design models based on adaptive-network-based fuzzy inference system (ANFIS) for the quasistatic analysis of three different coplanar strip line structures are presented. These strip line structures are conventional coplanar strip lines, asymmetrical coplanar strip lines with infinitely wide strip, and asymmetrical coplanar strip lines with infinitely thick dielectric substrate. The design parameters of the proposed ANFIS models are optimally determined by using four different optimization algorithms, hybrid learning, simulated annealing, least-squares, and genetic algorithm. When the performances of ANFIS models are compared with each other, the best results for training and test are obtained from the models trained with hybrid learning algorithm. There is a good agreement among the results of ANFIS models, quasistatic analysis, a full-wave electromagnetic simulator IE3D, and experimental works realized in this study. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1990,1996, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25372 [source]

Adaptive neuro-fuzzy models for the quasi-static analysis of microstrip line

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 5 2008
Celal Yildiz
Abstract This article presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the effective permittivities and characteristic impedances of microstrip lines. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It has the advantages of expert knowledge of FISs and learning capability of artificial neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of ANFIS. The results of ANFIS are compared with the results of the experimental works, quasi-static methods, and a commercial electromagnetic simulator IE3D. There is very good agreement among the results of ANFIS models and quasi-static methods, IE3D, and experimental works. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1191,1196, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23322 [source]

Adaptive neuro-fuzzy inference system for computing patch radius of circular microstrip antennas

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 8 2006
Kerim Guney
Abstract A new method based on the adaptive neuro-fuzzy inference system (ANFIS) is presented to calculate accurately the patch radius of circular microstrip antennas (MSAs). ANFIS combines the benefits of artificial neural networks (ANNs) and fuzzy inference systems (FISs) in a single model. A hybrid learning algorithm based on the least-squares method (LSM) and the backpropagation algorithm is used to identify the parameters of ANFIS. The results of ANFIS are in very good agreement with the experimental results reported elsewhere. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1606,1610, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21695 [source]

How to optimize the TS-fuzzy knowledge base to achieve desired performances: Accuracy and robustness

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 1 2008
A. Soukkou
Abstract Designing an effective criterion/learning to find the best rule and optimal structure is a major problem in the design process of fuzzy neural controller. In this paper, we introduce a new robust model of Takagi Sugeno fuzzy logic controller. A hybrid learning algorithm, called hybrid approach to fuzzy supervised learning (HAFSL), which combines the genetic algorithm (GA) and gradient descent technique (GD) is proposed for constructing an efficient and robust fuzzy neural network controller (FNNC). Two phases of design and learning process are presented in this work. A GA is used for finding near optimal structure/parameters of the FNNC that minimizes the number of rules (initialization procedure). The second stage of learning algorithm uses the backpropagation algorithm based on GD method to fine tune the consequent parameters of the controller. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the HAFSL are compared to these found by the traditional PI with genetic optimization (GA-PI). Simulations demonstrate that the proposed HAFSL and GA-PI algorithms have good generalization capabilities and robustness on the water bath temperature control system. Copyright © 2007 John Wiley & Sons, Ltd. [source]