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

Розробка нейромережевих і нечітких моделей багатомасових електромеханічних систем

The study objective was to construct models of multimass electromechanical systems using neural nets, fuzzy inference systems and hybrid networks by means of MATLAB tools. A model of a system in a form of a neural net or a neuro-fuzzy inference system was constructed on the basis of known input signals and signals measured at the system output. Methods of the theory of artificial neural nets and methods of the fuzzy modeling technology were used in the study.A neural net for solving the problem of identification of the electromechanical systems with complex kinematic connections was synthesized using the Neural Network Toolbox application package of the MATLAB system. A possibility of solving the identification problem using an approximating fuzzy system using the Fuzzy Logic Toolbox package was considered. A hybrid network was synthesized and implemented in a form of an adaptive neuro-fuzzy inference system using the ANFIS editor. Recommendations for choosing parameters that have the most significant effect on identification accuracy when applying the methods under consideration were given. It was shown that the use of neural nets and adaptive neuro-fuzzy inference systems makes it possible to identify systems with accuracy of 2 to 4%.As a result of the conducted studies, efficiency of application of neural nets, fuzzy inference systems and hybrid nets to identification of systems with complex kinematic connections in the presence of "input-output" information was shown. The neural-network, fuzzy and neuro-fuzzy models of two-mass electromechanical systems were synthesized with the use of modern software tools.The considered approach to using artificial intelligence technologies, that is neural nets and fuzzy logic is a promising line of construction of appropriate neural-network and neuro-fuzzy models of technical objects and systems. The study results can be used in synthesis of regulators for the systems with complex kinematic connections to ensure their high performance.Целью работы является построение моделей многомассовых электромеханических систем с применением нейронных сетей, систем нечеткого вывода и гибридных сетей инструментальными средствами MATLAB. Модель системы в виде нейронной сети или системы нейро-нечеткого вывода строится на основе известных входных сигналов и измеренных сигналов на выходе системы. При проведении исследований использованы методы теории искусственных нейронных сетей и методы технологии нечеткого моделирования.Выполнен синтез нейронной сети для решения задачи идентификации электромеханической системы со сложными кинематическими связями с применением пакета прикладных программ Neural Network Toolbox системы MATLAB. Рассмотрена возможность решения задачи идентификации с помощью нечеткой аппроксимирующей системы с использованием пакета Fuzzy Logic Toolbox. Проведен синтез гибридные сети, реализованной в форме адаптивной систем нейро-нечеткого вывода с применением редактора ANFIS. Даны рекомендации по выбору параметров, которые наиболее существенно влияют на точность идентификации при применении рассмотренных методов. Показано, что использование нейронных сетей и адаптивных систем нейро-нечеткого вывода позволяет выполнять идентификацию систем с точностью 4–5 %.В результате проведенных исследований показана эффективность применения нейронных сетей, систем нечеткого вывода и гибридных сетей для идентификации систем со сложными кинематическими связями при наличии информации «вход-выход». Выполнен синтез нейросетевой, нечеткой и нейро-нечеткой моделей двухмассовой электромеханической системы с использованием современных программных средств.Рассмотрен подход использования технологий искусственного интеллекта – нейронных сетей и нечеткой логики является перспективным направлением для построения соответствующих нейросетевых и нейро-нечетких моделей технологических объектов и систем. Результаты исследований могут быть использованы при синтезе регуляторов систем со сложными кинематическими связями для обеспечения высоких показателей качества функционирования системМетою роботи є побудова моделей багатомасових електромеханічних систем з застосуванням нейронних мереж, систем нечіткого висновку і гібридних мереж інструментальними засобами MATLAB. Модель системи у вигляді нейронної мережі або системи нейро-нечіткого висновку будується на основі відомих вхідних сигналів і виміряних сигналів на виході системи. При проведенні досліджень використані методи теорії штучних нейронних мереж і методи технології нечіткого моделювання.Виконано синтез нейронної мережі для вирішення завдання ідентифікації електромеханічної системи із складними кінематичними зв’язками з застосуванням пакету прикладних програм Neural Network Toolbox системи MATLAB. Розглянуто можливість вирішення задачі ідентифікації за допомогою нечіткої апроксимуючої системи з використанням пакету Fuzzy Logic Toolbox. Проведено синтез гібридні мережі, реалізованої у формі адаптивної систем нейро-нечіткого висновку з застосуванням редактора ANFIS. Надано рекомендації з вибору параметрів, що найбільш суттєво впливають на точності ідентифікації при застосуванні розглянутих методів. Показано, що використання нейронних мереж і адаптивних систем нейро-нечіткого висновку дозволяє виконувати ідентифікацію систем з точністю 4–5 %.В результаті проведених досліджень показана ефективність застосування нейронних мереж, систем нечіткого висновку і гібридних мереж для ідентифікації систем із складними кінематичними зв’язками при наявності інформації «вхід-вихід». Виконано синтез нейромережевої, нечіткої і нейро-нечіткої моделей двомасової електромеханічної системи з використанням сучасних програмних засобів.Розглянутий підхід використання технологій штучного інтелекту – нейронних мереж і нечіткої логіки – є перспективним напрямом для побудови відповідних нейромережевих і нейро-нечітких моделей технологічних об'єктів і систем. Результати досліджень можуть бути використані при синтезі регуляторів систем із складними кінематичними зв’язками для забезпечення високих показників якості функціонування систе

Neuro-Fuzzy Computing System with the Capacity of Implementation on Memristor-Crossbar and Optimization-Free Hardware Training

In this paper, first we present a new explanation for the relation between logical circuits and artificial neural networks, logical circuits and fuzzy logic, and artificial neural networks and fuzzy inference systems. Then, based on these results, we propose a new neuro-fuzzy computing system which can effectively be implemented on the memristor-crossbar structure. One important feature of the proposed system is that its hardware can directly be trained using the Hebbian learning rule and without the need to any optimization. The system also has a very good capability to deal with huge number of input-out training data without facing problems like overtraining.Comment: 16 pages, 11 images, submitted to IEEE Trans. on Fuzzy system

Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

Bibliometric Mapping of the Computational Intelligence Field

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation

"A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?"

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.