Analog and digital image enhancement using fuzzy expert systems

 Image enhancement is a field that is being used in various areas and disciplines. Advances in computers, microcontrollers and DSP boards have opened new horizons to digital image processing, and have opened many avenues to the design and implementation of new innovative techniques. This paper compares image enhancement via the modification of the probability density function of the gray levels with the new techniques that involves the use of knowledge-base (fuzzy expert) systems that are capable of mimicking the behavior of a human expert. A fuzzy expert system based software for image enhancement, called SmartPhotoLab has been introduced for the above purpose. Present address: A. El-Osery Dept. of Electrical Engineering, New Mexico Tech, Workman Center Rm. 247 801 Leroy place, Socorro, NM 87801 e-mail: elosery@ee.nmt.edu. This work was supported in parts by NASA grants no. NAG2–1196 and 2-1480.     

A design for a class of nonlinear systems with delay time using fuzzy logic

 The goal of this paper is to design a controller for a class of nonlinear systems with delay time using fuzzy logic. The control scheme considered in this paper integrates a fuzzy component and a sliding control component. In the former, the fuzzy system can be considered as a universal approximator to approximate the unknown functions in plant. In the latter, a variable structure control with a sector guarantees the global stability of the closed-loop system when a variable, involving tracking error, travels outside of the sector. The adaptive laws to adjust the parameters in the system are developed based on the Lyapunov synthesis approach. It is shown that the proposed adaptive controller guarantees tracking error, between the outputs of the considered system and desired␣values, to be asymptotical in decay.     

Hybrid expert systems: A survey of current approaches and applications

This paper is a statistical analysis of hybrid expert system approaches and their applications but more specifically connectionist and neuro-fuzzy system oriented articles are considered. The current survey of hybrid expert systems is based on the classification of articles from 1988 to 2010. Present analysis includes 91 articles from related academic journals, conference proceedings and literature reviews. Our results show an increase in the number of recent publications which is an indication of gaining popularity on the part of hybrid expert systems. This increase in the articles is mainly in neuro-fuzzy and rough neural expert systems’ areas. We also observe that many new industrial applications are developed using hybrid expert systems recently.     

The design process of expert systems development: some concerns

Abstract: A discussion is presented of why some expert systems that organizations have developed are not successful. The concept of design process plays a very significant role at the outset of the expert system development process. This concept has not been the subject of much debate and attention in expert systems development. From the author's point of view, one of the main issues is how the designer (knowledge engineer) thinks about the design process. In general, the designer's process is influenced by the knowledge engineer's conception. This paper endeavors to disclose some of the main factors related to the knowledge engineer's conception of the design process and an attempt is made to put forward a conceptual model of the expert system design process. This conceptual model is an initial step towards a successful implementation of expert system projects.     

Expert maintenance systems in telecommunication networks

Our survey of some 40 network maintenance expert systems reveals theri main shortcoming, which is the difficulty to acquire troubleshooting knowledge both when initializing the expert system and after its deployment. Additionally, the state-of-the-art troubleshooting expert systems do not optimize troubleshooting cost. We present theAO ^* algorithm to generate a network troubleshooting expert system which minimizes the expected troubleshooting cost and learns better troubleshooting techniques during its operation.     

An investigation of expert systems for die design

This article introduces fuzzy set theory to process the design details of the uncertain portion in die design, and assist the designer to transform those design items with fuzziness into those with definite and reasonable design attributes. For the design parameters of die block thickness, die clearance angle and die sets choice in die design, which possess intermediate features, fuzzy cluster analysis is used to obtain the design attributes. As for single-sided die clearance, stripper pressure and guide bushing-type die design, whose theory or empirical formulas possess uncertainty coefficients or preference design parameters, the fuzzy weighted average method is adopted to obtain the feature parameters that conform with the die design requirement. This study established an expert system prototype to combine the aforementioned uncertainty problems into two kinds of die design, and help the designer obtain a definite design strategy when faced with uncertain design items.     

About axiomatic systems of product fuzzy logic

 This short paper has two goals. The first is to show a new axiomatic system of product fuzzy logic with only one non-BL axiom which has only two variables. The second goal is to prove that there cannot be any axiomatic system of the product fuzzy logic with single non-BL axiom with only one variable.     

Fuzzy observer design using linear matrix inequalities for fuzzy closed-loop control systems

Linear observers play an important role in modern control theory and practice. A systematic design method of fuzzy observers would be important for fuzzy control as well. The fuzzy observer is designed by solving linear matrix inequalities (LMI) that represent control performance such as disturbance rejection and robust stability. Our approach in designing the fuzzy observer is based on the LMI formulation of the stability conditions for closed-loop Takagi-Sugeno (T-S) fuzzy systems, when states are not available for measurement of feedback. We present a new approach, which is to design an observer based on fuzzy implications, with fuzzy sets in the antecedents, and an asymptotic observer in the consequents. Each fuzzy rule is responsible for observing the states of a locally linear subsystem. An example illustrates the design procedure.     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

Solving fuzzy equations using evolutionary algorithms and neural nets

 In this paper we use evolutionary algorithms and neural nets to solve fuzzy equations. In Part I we: (1) first introduce our three solution methods for solving the fuzzy linear equation AˉXˉ + Bˉ= Cˉ; for Xˉ and (2) then survey the results for the fuzzy quadratic equations, fuzzy differential equations, fuzzy difference equations, fuzzy partial differential equations, systems of fuzzy linear equations, and fuzzy integral equations; and (3) apply an evolutionary algorithm to construct one of the solution types for the fuzzy eigenvalue problem. In Part II we: (1) first discuss how to design and train a neural net to solve AˉXˉ + Bˉ= Cˉ for Xˉ and (2) then survey the results for systems of fuzzy linear equations and the fuzzy quadratic.     

Multilevel fuzzy relational systems: structure and identification

 Existing fuzzy relational equations (FRE) typically possess an evident single-level structure, where no consequence part of the rule being modeled, is used as a fact to another rule. Corresponding to multistage fuzzy reasoning, a natural extension of traditional fuzzy relational systems (FRS) is to introduce some intermediate levels of processing governed by enhanced FRE's so that the structure resulted becomes multilevel or multistage. Three basic multilevel FRS structures, namely, incremental, aggregated, and cascaded, are considered in this paper and they correspond to different reasoning mechanisms being frequently used by human beings in daily life. While the research works on multilevel FRS are sparse and our ability to solve a system of multilevel FRE's in a purely analytical manner is very limited, we address the identification problem from an optimization approach and introduce three fuzzy neural models. The proposed models consist of single-level FRS modules that are arranged in different hierarchical manners. Each module can be realized by Lin and Lee's fuzzy neural model for implementing the Mamdani fuzzy inference. We have particularly addressed the problem of how to distribute the input variables to different (levels of) relational modules for the incremental and aggregated models. In addition, the new models can learn a complete multistage fuzzy rule set from stipulated data pairs using structural and parameter learning. The effectiveness of the multilevel models has been demonstrated through various benchmarking problems. It can be generally concluded that the new models are distinctive in learning, generalization, and robustness.     

Analytic fuzzy tableaux

 In this paper Beth–Smullyan's tableaux method is extended to the fuzzy propositional logic. The fuzzy tableaux method is based on the concepts of t-truth and extended graded formula. As in classical logic, it is a refutation procedure. A closed fuzzy tableau beginning with the extended graded formula [r, A] asserting that this is not t-true, is a tableau proof of the graded formula (A, r). The theorems of soundness, completeness, and decidability are proved.     

A fuzzy expert system for the early warning of accidents due to driver hypo-vigilance

In this paper a fuzzy expert system for the prediction of hypovigilance-related accidents is presented. The system uses physiological modalities in order to detect signs of extreme hypovigilance. An advantage of such a system is its extensibility regarding the physiological modalities and features that it can use as inputs. In that way, even though at present only eyelid-related features are exploited, in the future and for prototypes designed for professionals other physiological modalities, such as EEG can be easily integrated into the existing system in order to make it more robust and reliable.     

An embedded fuzzy expert system for adaptive WFQ scheduling of IEEE 802.16 networks

WiMAX is a futuristic technology which provides simultaneous support for web, video, and voice applications. WiMAX networks are best suitable to real time traffic however the quantity of non real time and best effort traffic cannot be neglected. Distribution of resources in such heterogeneous applications is therefore a challenging task. There are many schedulers available for WiMAX but adaptive and adequate schedulers are still in growing stage of development. This paper introduces a novel method using which a system is developed based on concepts of fuzzy logic to schedule traffic in WiMAX networks. The proposed fuzzy expert system simplifies fair allocation of resources to real as well as non real time traffic. The implementation is based on changing the weights of the queues serving real and non- real time traffic adaptively. New weights will be calculated for each bandwidth request made to base station and these weights will in turn decide amount of bandwidth allocated to different traffic classes. The weights are calculated based on three parameters that are amount of real time and non real time traffic in queues, change in throughput requirement for non real time flows and latency requirement of real time input data. Results obtained by virtue of simulations justify the significance of the proposed method.     

How to make constrained fuzzy arithmetic efficient

 In the standard fuzzy arithmetic, the vagueness of fuzzy quantities always increases. G. J. Klir [2, 3] suggests an alternative – the constrained fuzzy arithmetic – which reduces this effect. On the other hand, it significantly increases the complexity of computations in comparison to the classical calculus of fuzzy quantities. So far, little attention was paid to the problems of implementation of the constrained fuzzy arithmetic, especially to its computational efficiency. We point out the related problems and outline the ways of their solution. We suggest to decompose the whole expression, classify all its subexpressions with respect to their individual computational complexity and precompute the corresponding subresults according to this classification.     

A reasoning method for a ship design expert system

Abstract: The ship design process is a highly data‐oriented, dynamic, iterative and multi‐stage algorithm. It utilizes multiple abstraction levels and concurrent engineering techniques. Specialized techniques for knowledge acquisition, knowledge representation and reasoning must be developed to solve these problems for a ship design expert system. Consequently, very few attempts have been made to model the ship design process using an expert system approach. The current work investigates a knowledge representation–reasoning technique for such a purpose. A knowledge‐based conceptual design was developed by utilizing a prototype approach and hierarchical decompositioning. An expert system program called ALDES (accommodation layout design expert system) was developed by using the CLIPS expert system shell and an object‐oriented user interface. The reasoning and knowledge representation methods of ALDES are explained in the paper. An application of the method is given for the general arrangement design of a containership.     

An efficient fuzzy system for traffic management in high-speed packet-switched networks

 In this paper we present a novel buffer management scheme based on fuzzy logic. We deal with the problem of managing traffic flows with different priorities within the same buffer. The aim is to guarantee the QoS of high-priority traffic, and at the same time exploit unused buffer resources to accommodate best-effort traffic in order to maximize the total throughput. The scheme we propose can be applied both to ATM and IP networks. The performance evaluation of the fuzzy priority control scheme shows that it outperforms any static threshold mechanism and, as far as the total throughput is concerned, it is very close to that of the push out mechanism considered in literature as an ideal mechanism. Finally, we address some implementation issues of the control system and propose the design of a new cost-effective VLSI fuzzy processor.     

Two techniques of sensitivity and uncertainty analysis of fuzzy expert systems

Problems characterized by qualitative uncertainty described by expert judgments can be addressed by the fuzzy logic modeling paradigm, structured within a so-called fuzzy expert system (FES) to handle and propagate the qualitative, linguistic assessments by the experts. Once constructed, the FES model should be verified to make sure that it represents correctly the experts’ knowledge. For FES verification, typically there is not enough data to support and compare directly the expert- and FES-inferred solutions. Thus, there is the necessity to develop indirect methods for determining whether the expert system model provides a proper representation of the expert knowledge. A possible way to proceed is to examine the importance of the different input factors in determining the output of the FES model and to verify whether it is in agreement with the expert conceptualization of the model. In this view, two sensitivity and uncertainty analysis techniques applicable to generic FES models are proposed in this paper with the objective of providing appropriate tools of verification in support of the experts in the FES design phase. To analyze the insights gained by using the proposed techniques, a case study concerning a FES developed in the field of human reliability analysis has been considered.     

A neural network and fuzzy rule base hybrid for pattern classification

 This paper presents a novel hybrid of the two complimentary technologies of soft computing viz. neural networks and fuzzy logic to design a fuzzy rule based pattern classifier for problems with higher dimensional feature spaces. The neural network component of the hybrid, which acts as a pre-processor, is designed to take care of the all-important issue of feature selection. To circumvent the disadvantages of the popular back propagation algorithm to train the neural network, a meta-heuristic viz. threshold accepting (TA) has been used instead. Then, a fuzzy rule based classifier takes over the classification task with a reduced feature set. A combinatorial optimisation problem is formulated to minimise the number of rules in the classifier while guaranteeing high classification power. A modified threshold accepting algorithm proposed elsewhere by the authors (Ravi V, Zimmermann H.-J. (2000) Eur J Oper Res 123: 16–28) has been employed to solve this optimization problem. The proposed methodology has been demonstrated for (1) the wine classification problem having 13 features and (2) the Wisconsin breast cancer determination problem having 9 features. On the basis of these examples the results seem to be very interesting, as there is no reduction in the classification power in either of the problems, despite the fact that some of the original features have been completely eliminated from the study. On the contrary, the chosen features in both the problems yielded 100% classification power in some cases.