• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1619-1624
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• 2012

If multiple Tags in the RFID System transmit their IDs to the Reader at the same time, tag identification time is delayed due to collisions. Therefore, to reduce the reader's identification time, an efficient anti-collision technology is needed. In this paper, we propose a hybrid anti-collision method based on the QT and DFSA. Then, the performances of proposed method are compared with the existing method through extensive simulations.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.127-139
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• 1996

In this paper we propose a new controller design method using hybrid fuzzy-neural netowrk and neural network identification in order ot control systems which are more and more getting nonlinearity. Proposed method performs, for a nonlinear plant with unknown functions, hybird identification using a fuzzy-neural network and a neural network, and then a stable nonlinear controller is designed with those identified informations. To identify a nonlinear function, which is directly related to input signals, we can use a neural network which is satisfied with the proposed stable condition. To identify a nonlinear function, which is not directly related to input signals, we can use a fuzzy-neural network which has excellent identification characteristics. In order to verify excellent control performances of the proposed method, we compare the porposed control method with a conventional neural network control method through simulations and experiments with one link manipulator.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2885-2887
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• 2005

We introduce a hybrid identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally design the IG-based fuzzy model we exploit a hybrid identification through genetic alrogithms(GAs) and Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the seleced input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of HCM clustering help determine the initial paramters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the inital parameters are tuned effectively with the aid of the GAs and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.735-743
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• 2002

In this paper, we propose three new text-independent speaker identification methods. At first, to exclude the frames not having enough features of speaker's vocal from calculation of the maximum likelihood, we propose the FS(Frame Selection) method. This approach selects the important frames by evaluating the difference between the biggest likelihood and the second in each frame, and uses only the frames in calculating the score of likelihood. Our secondly proposed, called the Hybrid, is a combined version of the FS and WMR(Weighting Model Rank). This method determines the claimed speaker using exponential function weights, instead of likelihood itself, only on the selected frames obtained from the FS method. The last proposed, called MWMR (Modified WMR), considers both original likelihood itself and its relative position, when the claimed speaker is determined. It is different from the WMR that take into account only the relative position of likelihood. Through the experiments of the speaker identification, we show that the all the proposed have higher identification rates than the ML. In addition, the Hybrid and MWMR have higher identification rate about 2% and about 3% than WMR, respectively.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.959-967
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• 2004

Jain et al. proposed the hybrid matching method which was combined the minutia-based matching method and the filter-bank based matching method. And, their experimental results proved the hybrid matching method was more effective than each of them. However, this hybrid method cannot utilize each peculiar advantage of two methods. The reason is that it gets the matching score by simply summing up each weighted matching score after executing two methods individually. In this paper, we propose new hybrid matching method. It mixes two matching methods during the feature extraction process. This new hybrid method has lower ERR than the filter-bank based method and higher ERR than the minutia-based method. So, we propose the adaptive hybrid scoring method, which selects the matching score in order to preserve the characteristics of two matching methods. Using this method, we can get lower ERR than the hybrid matcher by Jain et al. Experimental results indicate that the proposed methods can improve the matching performance up to about 1% in ERR.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.1
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• pp.33-44
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• 2012

A class of recurrent neural networks is proposed and proven to be capable of identifying any discrete-time dynamical system. The applications of the proposed network are addressed in the encoding, identification, and extraction of finite state automata. Simulation results show that the identification of finite state automata using the proposed network, trained by the hybrid greedy simulated annealing with a modified error function in the learning stage, exhibits generally better performance than other conventional identification schemes.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.48-55
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• 2008

In this paper, we propose a hybrid fuzzy controller for high performance. Hybrid fuzzy controller are combined Fuzzy and PID controller. In tuning the controller, the parameters of PID and the factors fuzzy controllers were obtained from the model identification and by using genetic algorithms, respectively. Simulation examples demonstrated a better performance of the proposed controller than conventional ones.