Designing Mamdani-Type Fuzzy Reasoning for Visualizing Prediction Problems Based on Collaborative Fuzzy Clustering

In this paper a collaborative fuzzy c-means (CFCM) is used to generate fuzzy rules for fuzzy inference systems to evaluate the time series model. CFCM helps system to integrate two or more different datasets having similar features which are collected at the different environment with the different time period and it integrates these datasets together in order to visualize some common patterns among the datasets. In order to do any mode of integration between datasets, there is a necessity to define the common features between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling

Color Textured Image Segmentation Using ICICM - Interval Type-2 Fuzzy C-Means Clustering Hybrid Approach

Segmentation is an essential process in image because of its wild application such as image analysis, medical image analysis, pattern reorganization, etc. Color and texture are most significant low-level features in an image. Normally, color-textured image segmentation consists of two steps: (i) extracting the feature and (ii) clustering the feature vector. This paper presents the hybrid approach for color texture segmentation using Haralick features extracted from the Integrated Color and Intensity Co-occurrence Matrix (ICICM). Then, Extended- Interval Type-2 Fuzzy C-means clustering algorithm is used to cluster the obtained feature vectors into several classes corresponding to the different regions of the textured image. Experimental results show that the proposed hybrid approach could obtain better cluster quality and segmentation results compared to state-of-art image segmentation algorithms

Towards flow cytometry data clustering on graphics processing units

Like many modern techniques for scientific analysis, flow cytometry produces massive amounts of data that must be analyzed and clustered intelligently to be useful. Current manual binning techniques are cumbersome and limited in both the quality and quantity of analyses produced. To address the quality of results, a new framework applying two different sets of clustering algorithms and inference methods are implemented. The two methods investigated are fuzzy c-means and minimum description length inference and k-medoids with BIC. These approaches lend themselves to large scale parallel processing. To address the computational demands, the Nvidia CUDA framework and Tesla architecture are utilized. The resulting performance demonstrated 1-2 orders of magnitude improvement over an equivalent sequential version. The quality of results is promising and motivates further research and development in this direction

Data mining using intelligent systems : an optimized weighted fuzzy decision tree approach

Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful. In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets. The techniques are used to provide flexible information processing capability for handling real-life situations. This thesis is concerned with the design, implementation, testing and application of these techniques to those datasets. The thesis also introduces a hybrid intelligent systems technique: Optimized Weighted Fuzzy Decision Tree (OWFDT) with the aim of improving Fuzzy Decision Trees (FDT) and solving practical problems. This thesis first proposes an optimized weighted fuzzy decision tree, incorporating the introduction of Fuzzy C-Means to fuzzify the input instances but keeping the expected labels crisp. This leads to a different output layer activation function and weight connection in the neural network (NN) structure obtained by mapping the FDT to the NN. A momentum term was also introduced into the learning process to train the weight connections to avoid oscillation or divergence. A new reasoning mechanism has been also proposed to combine the constructed tree with those weights which had been optimized in the learning process. This thesis also makes a comparison between the OWFDT and two benchmark algorithms, Fuzzy ID3 and weighted FDT. SIx datasets ranging from material science to medical and civil engineering were introduced as case study applications. These datasets involve classification of composite material failure mechanism, classification of electrocorticography (ECoG)/Electroencephalogram (EEG) signals, eye bacteria prediction and wave overtopping prediction. Different intelligent systems techniques were used to cluster the patterns and predict the classes although OWFDT was used to design classifiers for all the datasets. In the material dataset, Self-Organizing Map and Fuzzy C-Means were used to cluster the acoustic event signals and classify those events to different failure mechanism, after the classification, OWFDT was introduced to design a classifier in an attempt to classify acoustic event signals. For the eye bacteria dataset, we use the bagging technique to improve the classification accuracy of Multilayer Perceptrons and Decision Trees. Bootstrap aggregating (bagging) to Decision Tree also helped to select those most important sensors (features) so that the dimension of the data could be reduced. Those features which were most important were used to grow the OWFDT and the curse of dimensionality problem could be solved using this approach. The last dataset, which is concerned with wave overtopping, was used to benchmark OWFDT with some other Intelligent Systems techniques, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Genetic Neural Mathematical Method (GNMM) and Fuzzy ARTMAP. Through analyzing these datasets using these Intelligent Systems Techniques, it has been shown that patterns and classes can be found or can be classified through combining those techniques together. OWFDT has also demonstrated its efficiency and effectiveness as compared with a conventional fuzzy Decision Tree and weighted fuzzy Decision Tree

On the Synthesis of fuzzy neural systems.

by Chung, Fu Lai.Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 166-174).ACKNOWLEDGEMENT --- p.iiiABSTRACT --- p.ivChapter 1. --- Introduction --- p.1Chapter 1.1 --- Integration of Fuzzy Systems and Neural Networks --- p.1Chapter 1.2 --- Objectives of the Research --- p.7Chapter 1.2.1 --- Fuzzification of Competitive Learning Algorithms --- p.7Chapter 1.2.2 --- Capacity Analysis of FAM and FRNS Models --- p.8Chapter 1.2.3 --- Structure and Parameter Identifications of FRNS --- p.9Chapter 1.3 --- Outline of the Thesis --- p.9Chapter 2. --- A Fuzzy System Primer --- p.11Chapter 2.1 --- Basic Concepts of Fuzzy Sets --- p.11Chapter 2.2 --- Fuzzy Set-Theoretic Operators --- p.15Chapter 2.3 --- "Linguistic Variable, Fuzzy Rule and Fuzzy Inference" --- p.19Chapter 2.4 --- Basic Structure of a Fuzzy System --- p.22Chapter 2.4.1 --- Fuzzifier --- p.22Chapter 2.4.2 --- Fuzzy Knowledge Base --- p.23Chapter 2.4.3 --- Fuzzy Inference Engine --- p.24Chapter 2.4.4 --- Defuzzifier --- p.28Chapter 2.5 --- Concluding Remarks --- p.29Chapter 3. --- Categories of Fuzzy Neural Systems --- p.30Chapter 3.1 --- Introduction --- p.30Chapter 3.2 --- Fuzzification of Neural Networks --- p.31Chapter 3.2.1 --- Fuzzy Membership Driven Models --- p.32Chapter 3.2.2 --- Fuzzy Operator Driven Models --- p.34Chapter 3.2.3 --- Fuzzy Arithmetic Driven Models --- p.35Chapter 3.3 --- Layered Network Implementation of Fuzzy Systems --- p.36Chapter 3.3.1 --- Mamdani's Fuzzy Systems --- p.36Chapter 3.3.2 --- Takagi and Sugeno's Fuzzy Systems --- p.37Chapter 3.3.3 --- Fuzzy Relation Based Fuzzy Systems --- p.38Chapter 3.4 --- Concluding Remarks --- p.40Chapter 4. --- Fuzzification of Competitive Learning Networks --- p.42Chapter 4.1 --- Introduction --- p.42Chapter 4.2 --- Crisp Competitive Learning --- p.44Chapter 4.2.1 --- Unsupervised Competitive Learning Algorithm --- p.46Chapter 4.2.2 --- Learning Vector Quantization Algorithm --- p.48Chapter 4.2.3 --- Frequency Sensitive Competitive Learning Algorithm --- p.50Chapter 4.3 --- Fuzzy Competitive Learning --- p.50Chapter 4.3.1 --- Unsupervised Fuzzy Competitive Learning Algorithm --- p.53Chapter 4.3.2 --- Fuzzy Learning Vector Quantization Algorithm --- p.54Chapter 4.3.3 --- Fuzzy Frequency Sensitive Competitive Learning Algorithm --- p.58Chapter 4.4 --- Stability of Fuzzy Competitive Learning --- p.58Chapter 4.5 --- Controlling the Fuzziness of Fuzzy Competitive Learning --- p.60Chapter 4.6 --- Interpretations of Fuzzy Competitive Learning Networks --- p.61Chapter 4.7 --- Simulation Results --- p.64Chapter 4.7.1 --- Performance of Fuzzy Competitive Learning Algorithms --- p.64Chapter 4.7.2 --- Performance of Monotonically Decreasing Fuzziness Control Scheme --- p.74Chapter 4.7.3 --- Interpretation of Trained Networks --- p.76Chapter 4.8 --- Concluding Remarks --- p.80Chapter 5. --- Capacity Analysis of Fuzzy Associative Memories --- p.82Chapter 5.1 --- Introduction --- p.82Chapter 5.2 --- Fuzzy Associative Memories (FAMs) --- p.83Chapter 5.3 --- Storing Multiple Rules in FAMs --- p.87Chapter 5.4 --- A High Capacity Encoding Scheme for FAMs --- p.90Chapter 5.5 --- Memory Capacity --- p.91Chapter 5.6 --- Rule Modification --- p.93Chapter 5.7 --- Inference Performance --- p.99Chapter 5.8 --- Concluding Remarks --- p.104Chapter 6. --- Capacity Analysis of Fuzzy Relational Neural Systems --- p.105Chapter 6.1 --- Introduction --- p.105Chapter 6.2 --- Fuzzy Relational Equations and Fuzzy Relational Neural Systems --- p.107Chapter 6.3 --- Solving a System of Fuzzy Relational Equations --- p.109Chapter 6.4 --- New Solvable Conditions --- p.112Chapter 6.4.1 --- Max-t Fuzzy Relational Equations --- p.112Chapter 6.4.2 --- Min-s Fuzzy Relational Equations --- p.117Chapter 6.5 --- Approximate Resolution --- p.119Chapter 6.6 --- System Capacity --- p.123Chapter 6.7 --- Inference Performance --- p.125Chapter 6.8 --- Concluding Remarks --- p.127Chapter 7. --- Structure and Parameter Identifications of Fuzzy Relational Neural Systems --- p.129Chapter 7.1 --- Introduction --- p.129Chapter 7.2 --- Modelling Nonlinear Dynamic Systems by Fuzzy Relational Equations --- p.131Chapter 7.3 --- A General FRNS Identification Algorithm --- p.138Chapter 7.4 --- An Evolutionary Computation Approach to Structure and Parameter Identifications --- p.139Chapter 7.4.1 --- Guided Evolutionary Simulated Annealing --- p.140Chapter 7.4.2 --- An Evolutionary Identification (EVIDENT) Algorithm --- p.143Chapter 7.5 --- Simulation Results --- p.146Chapter 7.6 --- Concluding Remarks --- p.158Chapter 8. --- Conclusions --- p.159Chapter 8.1 --- Summary of Contributions --- p.160Chapter 8.1.1 --- Fuzzy Competitive Learning --- p.160Chapter 8.1.2 --- Capacity Analysis of FAM and FRNS --- p.160Chapter 8.1.3 --- Numerical Identification of FRNS --- p.161Chapter 8.2 --- Further Investigations --- p.162Appendix A Publication List of the Candidate --- p.164BIBLIOGRAPHY --- p.16

Reasoning with linguistic preferences using NPN logic

Negative-positive-neutral logic provides an alternative framework for fuzzy cognitive maps development and decision analysis. This paper reviews basic notion of NPN logic and NPN relations and proposes adaptive approach to causality weights assessment. It employs linguistic models of causality weights activated by measurement-based fuzzy cognitive maps' concepts values. These models allow for quasi-dynamical adaptation to the change of concepts values, providing deeper understanding of possible side effects. Since in the real-world environments almost every decision has its consequences, presenting very valuable portion of information upon which we also make our decisions, the knowledge about the side effects enables more reliable decision analysis and directs actions of decision maker