scholarly journals Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake

2007 ◽  
pp. 123-157 ◽  
Author(s):  
Martha Carreño ◽  
Omar Cardona ◽  
Alex Barbat
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Fuzzy Rule ◽  
Neuro Fuzzy ◽  
Computational Intelligence Technique ◽  
Artificial Neural ◽  
Fuzzy Assessment ◽  
Rule Bases ◽  
Building Occupancy

This chapter describes the algorithmic basis of a computational intelligence technique, based on a neuro-fuzzy system, developed with the objective of assisting nonexpert professionals of building construction to evaluate the damage and safety of buildings after strong earthquakes, facilitating decision-making during the emergency response phase on their habitability and reparability. A hybrid neuro-fuzzy system is proposed, based on a special three-layer feedforward artificial neural network and fuzzy rule bases. The inputs to the system are fuzzy sets, taking into account that the damage levels of the structural components are linguistic variables, defined by means of qualifications such as slight, moderate or severe, which are very appropriate to handle subjective and incomplete information. The chapter is a contribution to the understanding of how soft computing applications, such as artificial neural networks and fuzzy sets, can be used to complex and urgent processes of engineering decision-making, like the building occupancy after a seismic disaster.

scholarly journals Computational Tool for Post-Earthquake Evaluation of Damage in Buildings

2010 ◽  
Vol 26 (1) ◽  
pp. 63-86 ◽  
Author(s):  
Martha L. Carreño ◽  
Omar D. Cardona ◽  
Alex H. Barbat
Keyword(s):  
Emergency Response ◽  
Safety Evaluation ◽  
Fuzzy Rule ◽  
Computational Tool ◽  
Strong Earthquakes ◽  
Neuro Fuzzy ◽  
Building Safety ◽  
Rule Bases ◽  
Building Occupancy ◽  
Seismic Disaster

A method and a computational tool oriented to assist the damage and safety evaluation of buildings after strong earthquakes is described in this article. The input of the model is the subjective and incomplete information on the building state, obtained by inspectors which are possibly not expert professionals of the field of building safety. The damage levels of the structural components are usually described by linguistic qualifications which can be adequately processed by computational intelligence techniques based on neuro-fuzzy systems what facilitate the complex and urgent tasks of engineering decision-making on the building occupancy after a seismic disaster. The hybrid neuro-fuzzy system used is based on a special three-layer feedforward artificial neural network and fuzzy rule bases and is an effective tool during the emergency response phase providing decisions about safety, habitability, and reparability of the buildings. Examples of application of the computer program are given for two different building classes.

Fuzzy-Rule Based Adaptive Data Warehouse

2012 ◽  
Vol 3 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Rajdev Tiwari ◽  
Anubhav Tiwari ◽  
Manu Pratap Singh
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Knowledge Workers ◽  
Fuzzy Rule ◽  
Data Warehouses ◽  
Large Reservoir ◽  
Inference System ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
New Generation

Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and information in it. This new generation DW may be called as Knowledge Warehouse (KW) shall exhibit decision making capabilities themselves and can also supplement the Decision Support Systems (DSS) in making decisions quickly and effortlessly. This paper proposes and simulates a fuzzy-rule based adaptive knowledge warehouse with capabilities to learn and represent implicit knowledge by means of adaptive neuro fuzzy inference system (ANFIS).

Fuzzy Rule Interpolation by the Conservation of Relative Fuzziness

2000 ◽  
Vol 4 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Laszlo T. Koczy ◽  
◽  
Kaoru Hirota ◽  
Tamas D. Gedeon ◽  
◽  
...  
Keyword(s):  
Fuzzy Sets ◽  
Interpolation Method ◽  
Fundamental Equation ◽  
Fuzzy Rule ◽  
Membership Functions ◽  
Inference Control ◽  
Multiple Dimensions ◽  
Linear Rule ◽  
Rule Bases ◽  
Refined Version

If the number of variables is growing the size of fuzzy rule bases increase exponentially. To reduce size and inference/control time, it is often necessary to deal with sparse rule bases. In such bases, classic algorithms such as the CRI of Zadeh and the Mamdani-method do not function. In such rule bases, rule interpolation techniques are necessary. The linear rule interpolation (KHinterpolation) based on the Fundamental Equation of Interpolation introduced by Koczy and Hirota is suitable for dealing with sparse bases, but this method often results in conclusions which are not directly interpretable, and need some further transformations. One of the possible ways to avoid this problem is the interpolation method based on the conservation of fuzziness, proposed recently by Gedeon and Koczy for trapezoidal fuzzy sets. In this paper, a refined version of that method will be presented that is fully in accordance with the Fundamental Equation, with extensions to multiple dimensions, and then to arbitrarily shaped membership functions. Several possibilities for the latter will be shown.

Fuzzy Expert System to Diagnose Diabetes Using S Weights for S Fuzzy Assessment Methodology

Fuzzy Systems ◽  
2017 ◽  
pp. 418-442
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Fuzzy Rule ◽  
Fuzzy Expert System ◽  
Assessment Methodology ◽  
Correct Decision ◽  
Fuzzy Similarity ◽  
Computer Based ◽  
Fuzzy Assessment

In the field of medicine decision making it is very important to deal with uncertainties, knowledge, and information. Decision making depends upon the experience, capability, and the observation of doctors. In the case of complex situations, it is very tough to give a correct decision. So computer-based procedure is very much essential. Fuzzy Expert System is used for decision making in the field of medicine. Fuzzy expert system consists of the following elements, fuzzification interface, S Fuzzy Assessment Methodology, and defuzzification. S Fuzzy Assessment Methodology uses the K Ratio to find overlap between membership function. To measure the similarity between fuzzy set, fuzzy number, and fuzzy rule, T Fuzzy similarity is used. Similar fuzzy sets are merged to form a common set; a new methodology was framed to identify the similarity between fuzzy rules with fuzzy numbers, and S Weights are to manage uncertainty in rules. S Weights use consequent and antecedent part of each rule. The efficiency of the proposed algorithm was implemented using MATLAB Fuzzy Logic tool box to construct a fuzzy expert system to diagnose diabetes.

scholarly journals Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task

2021 ◽  
Vol 14 (1) ◽  
pp. 123-129
Author(s):  
Yevgeniy Bodyanskiy ◽  
Anastasiia Deineko ◽  
Iryna Pliss ◽  
Olha Chala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Hybrid Learning ◽  
Medical Diagnostics ◽  
Speed Of Information Processing ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Overlapping Classes

Background: The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed. Methods: Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced. Results: The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes. Conclusion: The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.

scholarly journals The Data-Driven Fuzzy System with Fuzzy Subtractive Clustering for Time Series Modeling

2020 ◽  
Vol 9 (3) ◽  
pp. 3357-3362
Keyword(s):  
Fuzzy System ◽  
Fuzzy Rule ◽  
Training Data ◽  
Subtractive Clustering ◽  
A Value ◽  
Testing Data ◽  
System Input ◽  
Input Variables ◽  
Rule Bases ◽  
Takagi Sugeno

The paper aims to identify input variables of fuzzy systems, generate fuzzy rule bases by using the fuzzy subtractive clustering, and apply fuzzy system of Takagi Sugeno to predict rice stocks in Indonesia. The monthly rice procurement dataset in the period January 2000 to March 2017 are divided into training data (January 2000 to March 2016 and testing data (April 2016 to March 2017). The results of identification of the fuzzy system input variables are lags as system input including . The Input-output clustering fuzzy subtractive and selecting optimal groups by using the cluster thigness measures indicator produced 4 fuzzy rules.The fuzzy system performance in the training data has a value of R2 of 0.8582, while the testing data produces an R2 of 0.7513.

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