Jacek Kluska

Family of fuzzy J-K flip-flops based on bounded product, bounded sum and complementation

This paper presents a concept of new fuzzy J-K flip-flops based on bounded product, bounded sum and fuzzy complementation operations. Relationships between various types of the J-K flip-flops are given and characteristics of them are graphically shown by computer simulation. Two examples of circuits able to memorize and fuzzy information processing using the proposed fuzzy J-K flip-flops are presented.

Probabilistic Neural Network Structure Reduction for Medical Data Classification

Probabilistic neural network (PNN) consists of the number of pattern neurons that equals the cardinality of the data set. The model design is therefore complex for large database classification problems. In this article, two effective PNN reduction procedures are introduced. In the first approach, the PNN’s pattern layer neurons are reduced by means of a k-means clustering procedure. The second method uses a support vector machines algorithm to select pattern layer nodes. Modified PNN networks are compared with the original model in medical data classification problems. The prediction ability expressed in terms of the 20% test set error for the networks is assessed. By means of the experiments, it is shown that the appropriate pruning of the pattern layer neurons in the PNN enhances the performance of the classifier.

Hardware Implementation of P1-TS Fuzzy Rule-Based Systems on FPGA

This paper presents an FPGA hardware implementation of a special case of the fuzzy rule-based system, called P1-TS. The novelty of this work is recursive hardware architecture. The recursive implementation of the rule-based system allows us to build a versatile digital circuit for which FPGA logic resources requirements are small and independent on the number of input variables. The number of inputs is only limited by the capacity of the memory that stores the consequents of the rules. In our implementation, increasing the number of variables by 1 approximately doubles calculation time of the hardware device. We use floating-point arithmetic which ensures a higher dynamic range and makes that there is no need to focus on normalizing variables values to fixed word length.

Transformation lemma on analytical modeling via takagi-sugeno fuzzy system and its applications

The work presents some applications of the transformation lemma on analytical modeling using the Takagi-Sugeno fuzzy rule-based system [7], which can be used for exact fuzzy modeling of some class of conventional systems. The examples are based on recent authors theorems [5], which provide necessary and sufficient conditions for transformation of fuzzy rules into the crisp model of the system and vice-versa. The fuzzy model represents the widely used Takagi-Sugeno fuzzy system with linear membership functions. The main attention is paid for usability of the results for control engineering community.

A New Method of Fuzzy Petri Net Synthesis and its Application for Control Systems Design

A new method of fuzzy Petri net synthesis and its application for control systems design is presented. The work of Misiurewicz [5] that describes directions for use some class of Petri net for binary control systems synthesis, became the inspiration for our work. Our approach is based on fuzzy logic [4] and provides significant benefits in comparison with the classical concept mentioned above. Both an architecture and dynamics of fuzzy Petri net are described. An example of the control systems design and its software implementation on PLC is presented.

CNC Milling Tool Head Imbalance Prediction Using Computational Intelligence Methods

In this paper, a mechanical imbalance prediction problem for a milling tool heads used in Computer Numerical Control (CNC) machines was studied. Four classes of the head imbalance were examined. The data set included 27334 records with 14 features in the time and frequency domains. The feature selection procedure was applied in order to extract the most significant attributes. Only 3 out of 14 attributes were selected and utilized for the representation of each signal. Seven computational intelligence methods were applied in the prediction task: K–Means clustering algorithm, probabilistic neural network, single decision tree, boosted decision trees, multilayer perceptron, radial basis function neural network and support vector machine. The accuracy, sensitivity and specificity were computed in order to asses the performance of the algorithms.

Prediction of radical hysterectomy complications for cervical cancer using computational intelligence methods

In this work, eleven classifiers were tested in the prediction of intra- and post-operative complications in women with cervical cancer. For the real data set the normalization of the input variables was applied, the feature selection was performed and the original data set was binarized. The simulation showed the best model satisfying the quality criteria such as: the average value and the standard deviation of the error, the area under ROC curve, sensitivity and specificity. The results can be useful in clinical practice.

Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

BackgroundComputational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5–year overall survival prediction in patients with cervical cancer treated by radical hysterectomy.MethodsThe data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model.ResultsThe best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse.ConclusionsThe PNN model is an effective tool for predicting 5–year overall survival in cervical cancer patients treated with radical hysterectomy.

Selected Applications of P1-TS Fuzzy Rule-Based Systems

In this paper, some results concerning analytical methods of fuzzy modeling, especially so called P1-TS fuzzy rule-based systems are described. The basic notions and facts concerning the theory of fuzzy systems are briefly recalled, including a method for overcoming or at least weakening the curse of dimensionality. A P1-TS system performing the function of the fuzzy JK flip-flop, as well as optimal controller for the 2nd order dynamical plant are described. Next, we show how to use the idea of P1-TS system for identification of some class of nonlinear dynamical systems. We briefly characterize FPGA hardware implementation of the P1-TS system. A result of a mobile robot navigation system design is described, as well. Finally, we show how to obtain a highly interpretable fuzzy classifier as a medical decision support system, by using both the theory of P1-TS system with a large number of inputs in conjunction with the idea of meta-rules, and gene expression programming method.

Computational Intelligence application in fasteners manufacturing

The paper describes the application of Computational Intelligence (CI) methods for knowledge discovery from data collected in manufacturing execution system (MES) operating in a fasteners manufacturing company. The purpose of the research is to find factors causing decrease in the efficiency of the fasteners production process. The structure and preparation phase of analyzed data, concerning daily work data of pushing machines for cold forging are presented. Chosen methodology, i.e. classification algorithms is briefly described. Experiments of finding relationships between production speed, material and product type in the form of if-then rules were performed. The results received a positive opinion of the company management board and give promising prospects for the CI methods implementation in the factory. It is planned to use the CI methods as a continuously working part of a platform for Intelligent Manufacturing System (IMS), which has been implemented in the factory.