Model fusion using fuzzy aggregation: Special applications to metal properties

To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Time series forecasting using a TSK fuzzy system tuned with simulated annealing

In this paper, a combination of a Takagi-Sugeno fuzzy system (TSK) and simulated annealing is used to predict well known time series by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules. The results of the proposed method are encouraging indicating that simulated annealing and fuzzy logic are able to combine well in time series prediction

An adaptive neuro fuzzy inference system to model the uniaxial compressive strength of cemented hydraulic backfill

Purpose. The purpose of this paper is to develop the models for predicting the uniaxial compressive strength (UCS) of cemented hydraulic backfill (CHB), a widely used technique for filling underground voids created by mining operations as it provides the high strength required for safe and economical working environment and allows the use of waste rock from mining operations as well as tailings from mineral processing plants as ingredients. Methods. In this study, different modelling techniques such as conventional linear, nonlinear multiple regression and one of the evolving soft computing methods, adaptive neuro fuzzy inference system (ANFIS), were used for the prediction of UCS, the main criterion used to design backfill recipe. Findings. Statistical performance indices used to evaluate the efficiency of the developed models indicated that the ANFIS model can effectively be implemented for designing CHB with desired UCS. As proved by the performance indicators ANFIS model gives more compatible results with the expert opinion and current literature than conventional modelling techniques. Originality. In order to construct the models a very large database, containing more than 1600 UCS test results, was used. In addition to widely used conventional regression based modelling techniques, one of the evolving soft computing methods, ANFIS was employed. Numerical examples showing the implementation of constructed models were provided. Practical implementation. As proved by the statistical performance indicators, the developed models can be used for a reliable prediction of the UCS of CHB. However, more accurate results can be achieved by expanding the database and by constructing improved models using the algorithm presented in this paper.Мета. Побудова моделей для прогнозування межі міцності при одноосьовому стисканні цементної гідравлічної закладки для заповнення вироблених просторів шахт. Методика. Для досягнення поставленої мети були використані різні методи моделювання: лінійна та нелінійна множинна регресія, а також порівняно недавно став популярним метод програмування – адаптивне нейронечітке логічне виведення (ANFIS). За їх допомогою було спрогнозовано зміну міцності на одноосьове стискання, що є ключовим показником для визначення складу закладної суміші. Для побудови моделей використана значна база даних, яка включає результати більш ніж 1600 випробувань на одноосьове стискання. Лабораторними дослідженнями також визначалися властивості закладних матеріалів і суміші. Результати. Модель ANFIS дала найкращу продуктивність з урахуванням статистичних показників ефективності, таких як середня абсолютна процентна похибка і змінний обліковий запис. Статистичні показники продуктивності, які використовуються для оцінки ефективності розроблених моделей, свідчать, що моделювання за допомогою ANFIS дозволяє отримати результати, які більше відповідають експертній оцінці та даним з сучасної літератури, ніж інформація, отримана за допомогою традиційного моделювання. Встановлено, що на відміну від регресивного моделювання, ANFIS не вимагає заздалегідь визначених математичних рівнянь для взаємозв’язку між вхідними та вихідними змінними і використовує наданий набір даних для ефективного визначення структури моделі. Наукова новизна. Вперше для прогнозування міцності при одноосьовому стисканні були використані не лише традиційні способи моделювання, засновані на регресії, а й інноваційний метод програмування – адаптивне нейронечітке логічне виведення ANFIS. У статті наведені чисельні приклади впровадження нових побудованих моделей. Практична значимість. Статистичні індикатори продуктивності показали, що розроблені моделі можуть бути використані для надійного прогнозування міцності при одноосьовому стисканні й оптимальної рецептури закладної суміші. Однак, щоб отримати більш точні результати, необхідно мати більш широку базу даних і створити більш досконалі моделі на основі алгоритму, запропонованому в даній статті.Цель. Построение моделей для прогнозирования предела прочности при одноосном сжатии цементной гидравлической закладки для заполнения выработанных пространств шахт. Методика. Для достижения поставленной цели были использованы различные методы моделирования: линейная и нелинейная множественная регрессия, а также сравнительно недавно ставший популярным метод программирования – адаптивный нейронечеткий логический вывод (ANFIS). С их помощью было спрогнозировано изменение прочности на одноосное сжатие, что является ключевым показателем для определения состава закладочной смеси. Для построения моделей использована обширная база данных, которая включает результаты более чем 1600 испытаний на одноосное сжатие. Лабораторными исследованиями также определялись свойства закладочных материалов и смеси. Результаты. Модель ANFIS дала наилучшую производительность с учетом статистических показателей эффективности, таких как средняя абсолютная процентная погрешность и переменная учетная запись. Статистические показатели производительности, используемые для оценки эффективности разработанных моделей, свидетельствуют, что моделирование с помощью ANFIS позволяет получить результаты, которые более соответствуют экспертной оценке и данным из современной литературы, чем информация, полученная при помощи традиционного моделирования. Установлено, что в отличие от регрессионного моделирования, ANFIS не требует заранее определенных математических уравнений для взаимосвязи между входными и выходными переменными и использует предоставленный набор данных для эффективного определения структуры модели. Научная новизна. Впервые для прогнозирования прочности при одноосном сжатии были использованы не только традиционные способы моделирования, основанные на регрессии, но и инновационный метод программирования – адаптивный нейронечеткий логический вывод ANFIS. В статье приведены численные примеры внедрения новых построенных моделей. Практическая значимость. Статистические индикаторы производительности показали, что разработанные модели могут быть использованы для надежного прогнозирования прочности при одноосном сжатии и оптимальной рецептуры закладочной смеси. Однако, чтобы получить более точные результаты, необходимо иметь более широкую базу данных и создать более совершенные модели на основе алгоритма, предложенного в данной статье.The authors thank the staff and the managers of Jinfeng underground gold mine for their helps and cooperation during field and laboratory studies. The company is also acknowledged for the permission to use and publish the data

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use

On Fuzzy Concepts

In this paper we try to combine two approaches. One is the theory of knowledge graphs in which concepts are represented by graphs. The other is the axiomatic theory of fuzzy sets (AFS). The discussion will focus on the idea of fuzzy concept. It will be argued that the fuzziness of a concept in natural language is mainly due to the difference in interpretation that people give to a certain word. As different interpretations lead to different knowledge graphs, the notion of fuzzy concept should be describable in terms of sets of graphs. This leads to a natural introduction of membership values for elements of graphs. Using these membership values we apply AFS theory as well as an alternative approach to calculate fuzzy decision trees, that can be used to determine the most relevant elements of a concept

A neural network architecture for implementation of expert systems for real time monitoring

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables.

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried out on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed. The rule extraction algorithms, Neurolinear, Neurorule, Trepan and Nefclass, have different characteristics with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree (rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional ifthen rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.Credit; Information systems; International; Systems;