Comparison of different strategies of utilizing fuzzy clustering in structure identification

Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the �first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and o¤er a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classi�cation problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches

High cycle fatigue life prediction of laser additive manufactured stainless steel:A machine learning approach

Variations in the high cycle fatigue response of laser powder bed fusion materials can be caused by the choice of processing and post-processing strategies. The numerous influencing factors arising from the process demand an effective and unified approach to fatigue property assessment. This work examines the use of a neuro-fuzzy-based machine learning method for predicting the high cycle fatigue life of laser powder bed fusion stainless steel 316L. A dataset, consisting of fatigue life data for samples subjected to varying processing conditions (laser power, scan speed and layer thickness), post-processing treatments (annealing and hot isostatic pressing) and cyclic stresses, was constructed for simulating a complex nonlinear input-output environment. The associated fracture mechanisms, including the modes of crack initiation and deformation, were characterised. Two models, by employing the processing/post-processing parameters and the static tensile properties respectively as the inputs, were developed from the training data. Despite the diverse fatigue and fracture properties, the models demonstrated good prediction accuracy when checked against the test data, and the computationally-derived fuzzy rules agree well with understanding of the fracture mechanisms. Direct application of the model to literature results, however, yielded a range of prediction accuracies because of the variability in the reported data. Retraining the model by incorporating the literature results into the dataset led to improved modelling performance.Accepted versio

Application of expert systems in project management decision aiding

The feasibility of developing an expert systems-based project management decision aid to enhance the performance of NASA project managers was assessed. The research effort included extensive literature reviews in the areas of project management, project management decision aiding, expert systems technology, and human-computer interface engineering. Literature reviews were augmented by focused interviews with NASA managers. Time estimation for project scheduling was identified as the target activity for decision augmentation, and a design was developed for an Integrated NASA System for Intelligent Time Estimation (INSITE). The proposed INSITE design was judged feasible with a low level of risk. A partial proof-of-concept experiment was performed and was successful. Specific conclusions drawn from the research and analyses are included. The INSITE concept is potentially applicable in any management sphere, commercial or government, where time estimation is required for project scheduling. As project scheduling is a nearly universal management activity, the range of possibilities is considerable. The INSITE concept also holds potential for enhancing other management tasks, especially in areas such as cost estimation, where estimation-by-analogy is already a proven method

Linearity Testing Against a Fuzzy Rule-based Model

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange Multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.fuzzy rule-based models, time series, linearity test, statistical inference

Using Building Blocks to Design Analog Neuro-Fuzzy Controllers

We present a parallel architecture for fuzzy controllers and a methodology for their realization as analog CMOS chips for low- and medium-precision applications. These chips can be made to learn through the adaptation of electrically controllable parameters guided by a dedicated hardware-compatible learning algorithm. Our designs emphasize simplicity at the circuit level—a prerequisite for increasing processor complexity and operation speed. Examples include a three-input, four-rule controller chip in 1.5-μm CMOS, single-poly, double-metal technology

Trade-off between accuracy and interpretability: Experience-oriented fuzzy modeling via reduced-set vectors

AbstractThis paper focuses on accuracy and interpretability issue of fuzzy model approaches. In order to balance the trade-off between both of the aspects, a new fuzzy model based on experience-oriented learning algorithm is proposed. Firstly, support vector regression (SVR) with presented Mercer kernels is employed to generate the initial fuzzy model and the available experience on the training data. Secondly, a bottom-up simplification algorithm is introduced to generate reduced-set vectors for simplifying the structure of the initial fuzzy model, at the same time the parameters of the simplified model derived are adjusted by a hybrid learning algorithm including linear ridge regression algorithm and gradient descent method based on a new performance measure. Finally, taking the results from two-dimensional sinc function approximation and fuzzy control of the bar and beam system, the proposed fuzzy model preserves nice accuracy and interpretability

PROTOTYPE DEFINITION THROUGH CONSENSUS ANALYSIS BETWEEN FUZZY C-MEANS AND ARCHETYPAL ANALYSIS

The general aim of cluster analysis is to build prototypes, or typologies of units that present similar characteristics. In this paper we propose an alternative approach based on consensus analysis of two different clustering methods to suitably obtain proto- types. The clustering methods used are fuzzy c-means (centre approach) and archetypal analysis (extreme approach). The consensus clustering is used to assess the correspon- dence between the clustering solutions obtained

Prototype definition through consensus analysis between fuzzy c-means and archetypal analysis

The general aim of cluster analysis is to build prototypes, or typologies of units that present similar characteristics. In this paper we propose an alternative approach based on consensus analysis of two different clustering methods to suitably obtain prototypes. The clustering methods used are fuzzy c-means (centre approach) and archetypal analysis (extreme approach). The consensus clustering is used to assess the correspondence between the clustering solutions obtained