COFESS: cooperative fuzzy expert systems for intelligent recognition on small computers

Limitations on the representation of knowledge, and hence the inference methods, naturally limit the domains of the first-generation expert systems. In response to these limitations, fuzzy expert systems or the second-generation of expert systems provide two essential and unique advantages in the design, development, and implementation of expert systems: (1) fuzzy knowledge representation and (2) fuzzy inference methods. It is suggested that second-generation expert systems be designed and implemented with the expressive powers of representation and improved inference methods based on fuzzy logic. A number of research topics and application areas are identified. >

Assessing the performance of academics at different levels is increasingly difficult to achieve using traditional methods based mainly on numerical scores in evaluating teaching and research activity. The indexing of academic performance in various international databases with impact indices at different scales has led to the need for advanced computer models, such as expert systems based on fuzzy logic, proposed in this research, which address the evaluation of teachers even in the face of imprecise information and under conditions of uncertainty. In this research, as a contribution and novelty, a fuzzy logic model was developed in which an algorithm was simulated and implemented in Matlab using the Mandami toolkit, which allows inference of the rules of fuzzy logic and visualization. 3D. The system implementation was done by software in Matlab environment, using systems with fuzzy Mandami logic. The result of this pilot study was to test and validate the proposed model through a graphical interface, giving the results according to minimum criteria and with additional explanations.

Implementing a fuzzy expert system in an artificial neural network

With the increasing need for flexibility and adaptivity in computerized systems, the application of fuzzy expert systems is becoming increasingly commonplace in today's industry. Fuzzy logic expert systems often improve performance by allowing knowledge to generalize without requiring the knowledge engineer to anticipate all possible situations. Thus, for many types of applications, soft such as Fuzzy logic can incur lower overhead in terms of representing and engineering task knowledge. My project investigates the application of fuzzy expert systems to motion tracking. Previous research showed that Fuzzy Logic can be used to track the motion of a brightly colored object against a dark background, with relatively low development and run-time costs. The system I am developing identifies, tracks, and predicts the motion of multiple objects using unique identification patterns against a dark background. My poster will describe the fuzzy inference systems for tracking and motion prediction of such objects. An essential step to obtaining the fuzzy inputs for the motion tracking fuzzy inference system is to use convolution correlation data to obtain the centers of mass of the objects. Image processing information from the region around the center of each object provides good fuzzy inputs for recognizing object patterns and determining orientation. I am investigating two fuzzy inference systems for motion tracking and prediction in order to identify their strengths and weaknesses. In the long run, I hope to exploit the strengths of both by dividing the task between the two approaches. The Adaptive Neuro Fuzzy Inference System (ANFIS) is effective for pattern recognition. Fuzzy Proportional Derivative (FPD) is useful for computing errors and tuning inferences. Cascading the two fuzzy systems allows the system first to acquire generalized patterns and then fine-tune them for error tolerance. To perform this work, I am using the Matlab fuzzy logic toolkit, which is a simple and flexible tool for designing fuzzy inference systems.

Artificial intelligence and cognitive science must look at the world of industrial-process control to find the technological reifications of the concept of mind.

A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

The work analyzes the world experience in the development of medical information technologies. The method of using fuzzy sets for the implementation of an information expert system for solving the problems of medical diagnostics, in particular, during assessing the degree of anatomical damage of the coronary bed in patients with various forms of coronary artery disease, has been developed. The practical value of the work lies in the possibility of using an automated expert system to solve the problems of medical diagnosis based on fuzzy logic when assessing the degree of anatomical damage of the coronary bed in patients with various forms of coronary artery disease.

Implementation of fuzzy logic systems and neural networks in industry

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

This article provides a high-level retrospective of type-2 fuzzy sets and fuzzy logic systems. It explains how type-2 fuzzy sets can be used to model membership function uncertainties, and how by doing this smoother performance can be obtained than by using type-1 fuzzy sets. It also summarizes the notation that should be used for type-2 fuzzy sets, describes four important mathematical representations for these fuzzy sets, explains the differences between type-1 and type-2 fuzzy logic systems and which of the four representations is most useful when designing an optimal type-2 fuzzy logic system, provides a very useful strategy for optimal designs of fuzzy logic systems – one that guarantees performance improvement as one goes from a type-1 fuzzy logic system to a type-2 fuzzy logic system design – , and describes four methods for simplifying the designs of type-2 fuzzy logic systems. Finally, it explains why type-2 fuzzy sets can capture two kinds of linguistic uncertainties simultaneously (the uncertainty of an individual and the uncertainties of a group about a word), whereas type-1 fuzzy sets cannot, and that such type-2 fuzzy set word models are what should be used to implement Zadeh’s Computing With Words paradigm.

Expert systems have been widely used in the last forty years to have less dependence on human experts to resolve certain problems and enhance decision-making process by using computers. These systems are used by many industries and governmental sectors. The aim of this research is to focus on the pros and cons of implementing expert systems in general, and identifying the factors affecting its successful implementation in particular. Furthermore, the identified factors were used to evaluate the differences in implementing the expert systems in four out of the eight sectors specified by the UAE Strategy for Artificial Intelligence, namely; education, health, transportation and traffic, and environment. The methodology adopted in this study is detailed content analysis. Ten general factors affecting the implementation of the expert systems were identified. In addition, specific factors affecting the implementation of expert systems as part of the wide implementation of the UAE Strategy for Artificial Intelligence were also defined. Moreover, this study highlighted the difficulty in identifying special factors affecting the implementation of expert systems for any specified sector.

Dr. Flynxz – A First Aid Mamdani-Sugeno-type fuzzy expert system for differential symptoms-based diagnosis

The presently developed paper deals with the design of garlic expert systems using machine learning algorithms to advice the farmers in villages through online. An expert system is a computer program that simulates the judgments and behavior of a human or an organization that has expert knowledge and experience in a particular field. Typically, such a system contains a knowledge base containing accumulated experience and a set of rules for applying the knowledge base to each particular situation that is described to the program. Machine learning is a branch of computer science covering software that uses data to improve it's accuracy at some given task. Conrad uses supervised learning, which is one component of machine learning. Artificial Bee Colony (ABC) Algorithm is one of the mostly used Machine Learning Technique. By using this ABC Algorithm we developed a new 'Garlic Expert Advisory System'. This system is mainly aimed to identify the diseases and disease management in garlic crop production to advise the farmers in the villages on line to obtain standardized yields. This advisory system is designed by using Java Server Pages (JSP) as front end and MYSQL as backend.

An offline rule based expert system is developed to assist in controlling voltage-VAr/reactive power in a modified IEEE 14 bus power system. Inference strategy adopted for expert system is based on fuzzy logic. The system non linearity and coupling effect is simulated forming a sensitivity tree. Use of fuzzy logic and fuzzy sets in modelling interconnected complex modern power system for accurate control of voltage profile and thereby for management and control of reactive power is shown here.