• Computers and Concrete
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• 제5권6호
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• pp.573-597
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• 2008

Among artificial intelligence-based computational techniques, adaptive neuro-fuzzy inference systems (ANFIS) are particularly suitable for modelling complex systems with known input-output data sets. Such systems can be efficient in modelling non-linear, complex and ambiguous behaviour of cement-based materials undergoing single, dual or multiple damage factors of different forms (chemical, physical and structural). Due to the well-known complexity of sulfate attack on cement-based materials, the current work investigates the use of ANFIS to model the behaviour of a wide range of self-consolidating concrete (SCC) mixture designs under various high-concentration sodium sulfate exposure regimes including full immersion, wetting-drying, partial immersion, freezing-thawing, and cyclic cold-hot conditions with or without sustained flexural loading. Three ANFIS models have been developed to predict the expansion, reduction in elastic dynamic modulus, and starting time of failure of the tested SCC specimens under the various high-concentration sodium sulfate exposure regimes. A fuzzy inference system was also developed to predict the level of aggression of environmental conditions associated with very severe sodium sulfate attack based on temperature, relative humidity and degree of wetting-drying. The results show that predictions of the ANFIS and fuzzy inference systems were rational and accurate, with errors not exceeding 5%. Sensitivity analyses showed that the trends of results given by the models had good agreement with actual experimental results and with thermal, mineralogical and micro-analytical studies.

• 한국지능시스템학회논문지
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• 제8권6호
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• pp.58-69
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• 1998

This paper introduces a novel neuro-fuzzy system based on the polynomial fuzzy neural network(PFNN) architecture. The PFNN consists of a set of if-then rules with appropriate membership functions whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection, based on the Group Method of DAta Handling is defined to overcome the overfitting problem in the modeling procedure. The hybrid genetic optimization method, which combines a genetic algorithm and the Simplex method, is developed to increase performance even if the length of a chromosome is reduced. A novel coding scheme is presented to describe fuzzy systems for a dynamic search rang in th GA. For a performance assessment of the PFNN inference system, three well-known problems are used for comparison with other methods. The results of these comparisons show that the PFNN inference system outperforms the other methods while it exhibits exceptional robustness characteristics.

• Computers and Concrete
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• 제21권6호
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.199-207
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• 2023

Trend Impact Analysis is a prominent hybrid method has been used in future studies with a modified surprise- free forecast. It considers experts' perceptions about how future events may change the surprise-free forecast. It is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using adaptive neuro-fuzzy inference system (ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.675-677
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• 1998

In this paper, we propose the fuzzy inference algorithm with multi-layer structure. MFIS(Multi-layer Fuzzy Inference System) uses PNN(Polynomial Neural networks) structure and the fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Hendling), and uses several types of polynomials such as linear, quadratic and cubic, as well as the biquadratic polynomial used in the GMDH. In the fuzzy inference method, the simplified and regression polynomial inference methods are used. Here, the regression polynomial inference is based on consequence of fuzzy rules with the polynomial equations such as linear, quadratic and cubic equation. Each node of the MFIS is defined as fuzzy rules and its structure is a kind of neuro-fuzzy structure. We use the training and testing data set to obtain a balance between the approximation and the generalization of process model. Several numerical examples are used to evaluate the performance of the our proposed model.

• 대한의용생체공학회:의공학회지
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• 제24권5호
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• pp.465-471
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• 2003

본 논문에서는 생체신호 파라미터들을 이용해서 만성적인 스트레스를 평가하는 방법을 개발하고자 하였다. Wistar 쥐에게 14 일간의 소음 스트레스를 부과하고, 매 시간마다 생체신호를 획득하였다. 생체신호로부터 추출한 파라미터들을 통합하기 위한 퍼지추론시스템을 구축하기 위하여, 적응형 뉴로-퍼지 추론시스템으로 퍼지추론시스템의 파라미터들을 구하였다. 훈련 데이터 집합 중 입력 데이터 집합은 생체신호로부터 추출한 파라미터들을, 출력 데이터 집합은 코티솔 호르몬의 생성량으로부터 추정한 목표값을 사용하였다. 퍼지추론시스템으로 생체신호 파라미터들을 통합하고, 그 결과를 24 시간마다 구분하여 Cosinor 분석법을 적용하여 생체리듬의 변화를 관찰하였다. 생체리듬이 깨어진 정도에 의해서 만성적인 스트레스를 평가하였다. 생체신호 파라미터들을 퍼지추론으로 통합하고, 그 결과에서 생체리듬을 분석하여 스트레스 정도를 계산했다. 휴식기의 스트레스 정도를 l이라고 가정하면, 소음 스트레스를 받은지 14일째 되는 날에는 1.37. 7일간의 회복 후에는 1.47의 스트레스 정도가 나왔다. 즉, 쥐는 14일간의 소음으로 휴식 때보다 37% 증가된 스트레스를 받았고, 7일의 회복기를 통해 스트레스로부터 회복되지 않았다.

• Smart Structures and Systems
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• 제15권4호
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• pp.931-947
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• 2015

This paper focuses on the design of an intelligent control system. The used techniques are based on Neuro Fuzzy approaches applied to a magnetorheological damper in order to reduce the vibrations over footbridges; it has been applied to the Science Museum Footbridge of Valladolid, particularly. A model of the footbridge and of the damper has been built using different simulation tools, and a successful comparison with the real footbridge and the real damper has been carried out. This simulated model has allowed the reproduction of the behaviour of the footbridge and damper when a pedestrian walks across the footbridge. Once it is determined that the simulation results are similar to real data, the control system is introduced into the model. In this sense, different strategies based on Neuro Fuzzy systems have been studied. In fact, an ANFIS (Artificial Neuro Fuzzy Inference System) method has also been used, in addition to an alternative Neuro Fuzzy approach. Several trials have been carried out, using both techniques, obtaining satisfactory results after using these techniques.

• 전자공학회논문지B
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• 제31B권5호
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• pp.149-161
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• 1994

The Max-Min CRI method , a traditional inference method , has three problems: subjective formulation of membership functions, error-prone weighting strategy, and inefficient compositional rule of inference. Because of these problems, there is an insurmountable error region between desired output and inferred output. To overcome these problems, we propose an Intelligent Neuro-Fuzzy System (INFS) based on fuzzy thoery and self-organizing functions of neural networks. INFS makes use of neural networks(Error Back Propagation) to solve the first problem, and NCRI(New Max-Min CRI) method for the second. With a proposed similarity measure, NCRI method is an improved method compared to the traditional Max-Min CRI method. For the last problem, we propose a new defuzzification method which combines only the appropriate rules produced by the rule selection level. Applying INFS to a D.C. series motor, we can conclude that the error region is reduced and NCRI method performs better than Max-Min CRI method.

• 전산구조공학
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• 제23권3호
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• pp.61-68
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• 2010

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2001년도 추계학술발표회요약집
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• pp.77-77
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• 2001