Extracting fuzzy rules for modeling of complex processes using neural networks

2013 ◽  
Vol 227 (12) ◽  
pp. 2861-2869 ◽  
Author(s):  
A Jamali ◽  
SJ Motevalli ◽  
N Nariman-zadeh
Keyword(s):  
Neural Networks ◽  
Fuzzy Rule ◽  
Structure Design ◽  
Fuzzy Rules ◽  
Group Method ◽  
Rule Base ◽  
Input Output ◽  
Output Data ◽  
Complex Processes ◽  
Linguistic Rules

Modeling of complex processes often leads to complex mathematical relationships between inputs and outputs, which do not reflect the influence of the independent variables on the output parameters. In this article, an innovative technique based on neural networks is presented to extract fuzzy linguistic rules for modeling some processes using some input–output data. In this way, genetic algorithm is used both for optimal structure design of those group method of data handling-type neural networks and for subsequent optimization of sub-bounds of fuzzy singleton antecedents to further optimize the obtained fuzzy rule base. Three different input–output data tables related to some complex problems of a nonlinear mathematical system, an explosive cutting process and the probability of failure estimation of a two mass-spring system are modeled by some fuzzy rules, using the technique discussed in this article.

Neural Networks

2012 ◽  
pp. 450-498 ◽  
Author(s):  
Siddhartha Bhattacharyya
Keyword(s):  
Neural Networks ◽  
Data Networks ◽  
Input Output ◽  
Training Set ◽  
Topology Preservation ◽  
Functional Characteristics ◽  
Output Data ◽  
Computing Paradigm ◽  
Mode Of Operation ◽  
Recent Trends

These networks generally operate in two different modes, viz., supervised and unsupervised modes. The supervised mode of operation requires a supervisor to train the network with a training set of data. Networks operating in unsupervised mode apply topology preservation techniques so as to learn inputs. Representative examples of networks following either of these two modes are presented with reference to their topologies, configurations, types of input-output data and functional characteristics. Recent trends in this computing paradigm are also reported with due regards to the application perspectives.

scholarly journals A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

scholarly journals Deciphering the regulation mechanism in biochemical networks by a systems-biology approach

2017 ◽  
Author(s):  
Bernardo A. Mello ◽  
Yuhai Tu
Keyword(s):  
Systems Biology ◽  
Molecular Mechanisms ◽  
Biochemical Networks ◽  
System Level ◽  
Regulation Mechanism ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Complex Processes ◽  
Regulation Mechanisms

To decipher molecular mechanisms in biological systems from system-level input-output data is challenging especially for complex processes that involve interactions among multiple components. Here, we study regulation of the multi-domain (P1-5) histidine kinase CheA by the MCP chemoreceptors. We develop a network model to describe dynamics of the system treating the receptor complex with CheW and P3P4P5 domains of CheA as a regulated enzyme with two substrates, P1 and ATP. The model enables us to search the hypothesis space systematically for the simplest possible regulation mechanism consistent with the available data. Our analysis reveals a novel dual regulation mechanism wherein besides regulating ATP binding the receptor activity has to regulate one other key reaction, either P1 binding or phosphotransfer between P1 and ATP. Furthermore, our study shows that the receptors only control kinetic rates of the enzyme without changing its equilibrium properties. Predictions are made for future experiments to distinguish the remaining two dual-regulation mechanisms. This systems-biology approach of combining modeling and a large input-output data-set should be applicable for studying other complex biological processes.

scholarly journals Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

2020 ◽  
Vol 10 (1) ◽  
pp. 57-69 ◽  
Author(s):  
Marcin Korytkowski ◽  
Roman Senkerik ◽  
Magdalena M. Scherer ◽  
Rafal A. Angryk ◽  
Miroslaw Kordos ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Differential Evolution Algorithm ◽  
Fuzzy Rule ◽  
Image Features ◽  
Population Diversity ◽  
Fuzzy Rules ◽  
Support Vector ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Novel Method

AbstractFast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.

Application of GMDH in Modeling of Manual Materials Handling Activities

1980 ◽  
Vol 24 (1) ◽  
pp. 647-647
Author(s):  
Onur M. Ulgen ◽  
Anil Mital
Keyword(s):  
Group Method ◽  
Data Handling ◽  
Input Output ◽  
Output Data ◽  
Materials Handling ◽  
Modeling Methods ◽  
Manual Materials Handling ◽  
Heuristic Technique ◽  
Structural Uncertainties ◽  
Non Linear Systems

This paper investigates the application of Group Method of Data Handling (GMDH) in modeling of Manual Materials Handling Activities (MMHA). GMDH is a powerful heuristic technique that can be applied for modeling complex non-linear systems with a large number of variables and parameters, structural uncertainties, and/or a limited amount of collected data. The technique explains the behaviour of a system based on input-output data and in that respect is similar to statistical modeling methods (Ivakhnenko, 1970; 1971).

scholarly journals Development of the principles of fuzzy rule-based system for hepatocelular carcinoma staging

2021 ◽  
pp. 3-13
Author(s):  
Masuma Mammadova ◽  
Nuru Bayramov ◽  
Zarifa Jabrayilova
Keyword(s):  
Input Data ◽  
Clinical Signs ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Second Phase ◽  
Rule Based ◽  
Rule Based System ◽  
Linguistic Rules ◽  
Clinical Conditions ◽  
Critical Situations

The article proposes the principles for the development of a fuzzy rule-based physician decision support system n to determine the stages of the most common hepatocellular carcinoma (HCC) among malignant tumors of liver. The stages of HCC, i.e., critical situations, are expressed by different combinations of clinical signs of input data and emerging clinical conditions. These combinations shape the multiplicity of possible situations (critical situations) by forming linguistic rules that are in fuzzy relations with one another. The article presents the task of developing a fuzzy rules-based system for HCC staging by classifying the set of possible situations into given classes. In order to solve the problem, fuzzy rules of clinical situations and critical situations deviated from them are developed according to the possible clinical signs of input data. The rules in accordance with the decision-making process are developed in two phases. In the first phase, three input data are developed: nine rules are developed to determine possible clinical conditions based on the number, size, and vascular invasion of tumor. In the second phase, seven rules are developed based on possible combinations of input data on the presence of lymph nodes and metastases in these nine clinical conditions. At this stage, the rules representing the fuzzification of results obtained are also described. The latter provide an interpretation of results and a decision on related stage of HCC. It also proposes a functional scheme of fuzzy rules-based system for HCC staging, and presents the working principle of structural blocks. The fuzzy rule-based system for HCC staging can be used to support physicians to make diagnostic and treatment decisions

FUZZY RULE EXTRACTION FROM INPUT/OUTPUT DATA

2004 ◽  
pp. 199-216
Author(s):  
L. T. KÓCZY ◽  
J. BOTZHEIM ◽  
A. B. RUANO ◽  
A. CHONG ◽  
T. D. GEDEON
Keyword(s):  
Fuzzy Rule ◽  
Rule Extraction ◽  
Input Output ◽  
Output Data

Rule Extraction from Data

1999 ◽  
Vol 3 (5) ◽  
pp. 339-340
Author(s):  
Takeshi Furuhashi ◽  
Keyword(s):  
Neural Networks ◽  
Fuzzy Rule ◽  
Classification Problem ◽  
Fuzzy Modeling ◽  
Rule Extraction ◽  
Fuzzy Rules ◽  
High Dimensional ◽  
Special Issue ◽  
Rule Sets ◽  
Artificial Neural

Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This special issue entitled"Rule Extraction from Data" is aimed at providing the readers with good insights into the advanced studies in the field of rule extraction from data using neural networks/fuzzy rule sets. I invited seven research papers best suited for the theme of this special issue. All the papers were reviewed rigorously by two reviewers each. The first paper proposes an interesting rule extraction method from data using neural networks. Ishikawa presents a combination of learning with an immediate critic and a structural learning with forgetting. This method is capable of generating skeletal networks for logical rule extraction from data with correct and wrong answers. The proposed method is applied to rule extraction from lense data. The second paper presents a new methodology for logical rule extraction based on transformation of MLP (multilayered perceptron) to a logical network. Duck et al. applied their C-MLP2LN to the Iris benchmark classification problem as well as real-world medical data with very good results. In the third paper, Geczy and Usui propose fuzzy rule extraction from trained artificial neural networks. The proposed algorithm is implied from their theoretical study, not from heuristics. Their study enables to initially consider derivation of crisp rules from trained artificial neural network, and in case of conflict, application of fuzzy rules. The proposed algorithm is experimentally demonstrated with the Iris benchmark classification problem. The fourth paper presents a new framework for fuzzy modeling using genetic algorithm. The authors have broken new ground of fuzzy rule extraction from neural networks. For the fuzzy modeling, they have proposed a particular type of neural networks containing nodes representing membership functions. In this fourth paper, the authors discuss input variable selection for the fuzzy modeling under multiple criteria with different importance. A target system with a strong nonlinearity is used for demonstrating the proposed method. Kasabov, et al. present, in the fifth paper, a method for extraction of fuzzy rules that have different level of abstraction depending on several modifiable thresholds. Explanation quality becomes better with higher threshold values. They apply the proposed method to the Iris benchmark classification problem and to a real world problem. J. Yen and W. Gillespie address interpretability issue of Takagi-Sugeno-Kang model, one of the most popular fuzzy mdoels, in the fifth paper. They propose a new approach of fuzzy modeling that ensures not only a high approximation of the input-output relationship in the data, but also good insights about the local behavior of the model. The proposed method is applied to fuzzy modeling of sinc function and Mackey-Glass chaotic time series data. The last paper discusses fuzzy rule extraction from numerical data for high-dimensional classification problems. H.Ishibuchi, et al. have been pioneering methods for classification of data using fuzzy rules and genetic algorithm. In this last paper, they introduced a new criterion, simplicity of each rule, together with the conventional ones, compactness of rule base and classification ability, for high-dimensional problem. The Iris data is used for demonstrating their new classification method. They applied it also to wine data and credit data. I hope that the readers will be encouraged to explore the frontier to establish a new paradigm in the field of knowledge representation and rule extraction.

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