Power control in CDMA networks based on soft computing methods

Power control is a fundamental procedure for CDMA mobile radio communication systems. In multiservice CDMA systems, power control should be used to minimise the transmission power of each connection, in order to limit the multiple access interference, while obtaining the desirable SIR levels. This paper starts from a transmitted-power allocation algorithm (TPAA) that considers a set of uplink transmissions, which should be supported by the system. In the sequel, the TPAA algorithm is used for training an Elman neural network, which, due to its internal characteristics, is applicable in the time critical context of power control. Simulations and numerical results are analysed for obtaining a solid basis for employing our scheme in the power control of CDMA systems.     

Fuzzy control of complex systems

 One of the biggest challenges of any control paradigm is being able to handle large complex systems. A system may be called large-scale or complex, here, if its dimension (order) is so high and its model (if available) is nonlinear, interconnected with uncertain information flow such that classical techniques of control theory cannot easily handle the system. From a control theoretical point of view, fuzzy logic has been intermixed with all the important aspects of systems theory - modeling, identification, analysis, stability, synthesis, filtering, and estimation. However, the application of fuzzy control to large-scale complex systems is not a trivial task by any means. For such systems the size of the rule base in a typical fuzzy control architecture will be nearly infinite. In this paper an attempt is made to break some new ground on the applications of fuzzy control to complex systems. A new rule base reduction approach is suggested to manage large inference engines. Notions of rule hierarchy and sensor data fusion are introduced and combined to achieve system’s goals. The technique has been implemented on an SGS Thomson W.A.R.P. chip for an inverted pendulum with wine-balancing application.     

A modified Elman neural network-based power controller in mobile communications systems

It has been well recognized that power control is an important technique to combat with the harmful near-far effect as well as increase the maximum user capacity of direct sequence code division multiple access (DS/CDMA) cellular systems. In this paper, we propose a modified Elman neural network (MENN)-based power control scheme, which can regulate the received power level at the base station. Unlike the conventional “bang–bang” and fuzzy logic power control, our MENN-based controller first identifies the inverse dynamical characteristics of mobile channel by adaptive on-line learning. The inverse channel model is then employed for power regulation to reduce large overshoots and shorten long rise time. Simulations show that the fluctuation of controlled received power levels can be smoothed with small channel tracking errors. Acknowledgments The authors would like to thank the anonymous reviewers for their insightful comments and constructive suggestions that have improved this paper.     

A new method for adaptive model-based control of non-linear dynamic plants using a neuro-fuzzy-fractal approach

 We describe in this paper a new method for adaptive model-based control of non-linear dynamic plants using Neural Networks, Fuzzy Logic and Fractal Theory. The new neuro-fuzzy-fractal method combines Soft Computing (SC) techniques with the concept of the fractal dimension for the domain of Non-Linear Dynamic Plant Control. The new method for adaptive model-based control has been implemented as a computer program to show that our neuro-fuzzy-fractal approach is a good alternative for controlling non-linear dynamic plants. We illustrate in this paper our new methodology with the case of controlling biochemical reactors in the food industry. For this case, we use mathematical models for the simulation of bacteria growth for several types of food. The goal of constructing these models is to capture the dynamics of bacteria population in food, so as to have a way of controlling this dynamics for industrial purposes.     

Fusion of soft and hard computing: multi-dimensional categorization of computationally intelligent hybrid systems

The concept of fusion of soft computing and hard computing has rapidly gained importance over the last few years. Soft computing is known as a complementary set of techniques such as neural networks, fuzzy systems, or evolutionary computation which are able to deal with uncertainty, partial truth, and imprecision. Hard computing, i.e., the huge set of traditional techniques, is usually seen as the antipode of soft computing. Fusion of soft and hard computing techniques aims at exploiting the particular advantages of both realms. This article introduces a multi-dimensional categorization scheme for fusion techniques and applies it by analyzing several fusion techniques where the soft computing part is realized by a neural network. The categorization scheme facilitates the discussion of advantages or drawbacks of certain fusion approaches, thus supporting the development of novel fusion techniques and applications.     

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 generalization of the counter-propagation neural network: a family of soft competitive basis function neural networks

 Based on combining neural network (NN) with fuzzy logical system (FLS), a new family of three-layer feedforward networks, called soft-competition basis function neural networks (SCBFs), is proposed under the framework of the counter-propagation (CP) network. The hidden layer of SCBFs is designed as competitive layer with soft competitive strategy. The output function of their hidden neuron is defined as basis function taking the form of fuzzy membership function. SCBFs possess the ability of functional approximation. They are fuzzy generalization of the CP network and functionally equivalent to TS-model of fuzzy logical system. Therefore, they can be regard as either a NN or a FLS. Their learning algorithms are also discussed in this paper. Finally, some experiments are given to test the performance of SCBFs.     

Lower bounds for the broadcast problem in mobile radio networks

Summary.  In this paper, we prove a lower bound on the number of rounds required by a deterministic distributed protocol for broadcasting a message in radio networks whose processors do not know the identities of their neighbors. Such an assumption captures the main characteristic of mobile and wireless environments [3], i.e., the instability of the network topology. For any distributed broadcast protocol Π, for any n and for any D≦n/2, we exhibit a network G with n nodes and diameter D such that the number of rounds needed by Π for broadcasting a message in G is Ω(D log n). The result still holds even if the processors in the network use a different program and know n and D. We also consider the version of the broadcast problem in which an arbitrary number of processors issue at the same time an identical message that has to be delivered to the other processors. In such a case we prove that, even assuming that the processors know the network topology, Ω(n) rounds are required for solving the problem on a complete network (D=1) with n processors. Received: August 1994 / Accepted: August 1996     

Soft computing in engineering design - A review

The present paper surveys the application of soft computing (SC) techniques in engineering design. Within this context, fuzzy logic (FL), genetic algorithms (GA) and artificial neural networks (ANN), as well as their fusion are reviewed in order to examine the capability of soft computing methods and techniques to effectively address various hard-to-solve design tasks and issues. Both these tasks and issues are studied in the first part of the paper accompanied by references to some results extracted from a survey performed for in some industrial enterprises. The second part of the paper makes an extensive review of the literature regarding the application of soft computing (SC) techniques in engineering design. Although this review cannot be collectively exhaustive, it may be considered as a valuable guide for researchers who are interested in the domain of engineering design and wish to explore the opportunities offered by fuzzy logic, artificial neural networks and genetic algorithms for further improvement of both the design outcome and the design process itself. An arithmetic method is used in order to evaluate the review results, to locate the research areas where SC has already given considerable results and to reveal new research opportunities.     

Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory

We describe in this paper a hybrid method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show that the neuro-fuzzy-fractal approach is a good alternative for controlling nonlinear dynamic systems. It is well known that chaotic and unstable behavior may occur for nonlinear systems. Normally, we will need to control this type of behavior to avoid structural problems with the system. We illustrate in this paper our new methodology with the case of controlling aircraft dynamic systems. For this case, we use mathematical models for the simulation of aircraft dynamics during flight. The goal of constructing these models is to capture the dynamics of the aircraft, so as to have a way of controlling this dynamics to avoid dangerous behavior of the aircraft dynamic system.     

User profiles and fuzzy logic for web retrieval issues

 We present a study of the role of user profiles using fuzzy logic in web retrieval processes. Flexibility for user interaction and for adaptation in profile construction becomes an important issue. We focus our study on user profiles, including creation, modification, storage, clustering and interpretation. We also consider the role of fuzzy logic and other soft computing techniques to improve user profiles. Extended profiles contain additional information related to the user that can be used to personalize and customize the retrieval process as well as the web site. Web mining processes can be carried out by means of fuzzy clustering of these extended profiles and fuzzy rule construction. Fuzzy inference can be used in order to modify queries and extract knowledge from profiles with marketing purposes within a web framework. An architecture of a portal that could support web mining technology is also presented.     

Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms

This paper presents two different power system stabilizers (PSSs) which are designed making use of neural fuzzy network and genetic algorithms (GAs). In both cases, GAs tune a conventional PSS on different operating conditions and then, the relationship between these points and the PSS parameters is learned by the ANFIS. ANFIS will select the PSS parameters based on machine loading conditions. The first stabilizer is adjusted minimizing an objective function based on ITAE index, while second stabilizer is adjusted minimizing an objective function based on pole-placement technique. The proposed stabilizers have been tested by performing simulations of the overall nonlinear system. Preliminary experimental results are shown.     

A roadmap of fuzzy logic in ATM,mobile and active networks: challenges of the new century

 ATM networking technology was conceived 20 years ago, and installations will reach their peak the next 10 years. Active networks will be the technology which will follow the ATM. In this article, we address the most important issues regarding recent advances and future perspectives in ATM, including IP/ATM integration, Active Networks, MobileActive Networks and the impact of fuzzy technology in solving the important problems of the above future networking technologies. In the new century it is imperative that we shift from a technology to an application (needs of consumers) focus, where ubiquitous and invisible (context-aware) computing will be a reality.     

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.     

Soft-computing combining of distributed power control algorithms

In traditional distributed power control (DPC) algorithms, every user in the system is treated in the same way, i.e., the same power control algorithm is applied to every user in the system. In this paper, we divide the users into different groups depending on their channel conditions and use different DPC accordingly. Our motivation comes from the fact that different DPC algorithms have its own advantages and drawbacks, and our aim in this paper is to “combine” the advantages of different DPC algorithms, and we use soft computing techniques for that. In the simulations results, we choose Foschini and Miljanic Algorithm in [3], which has relatively fast convergence but is not robust against time-varying link gain changes and CIR estimation errors, and fixed step algorithm of Kim [3], which is robust but its convergence is slow. By “combining” these two algorithms using soft computing techniques, the resulting algorithm has fast convergence and is robust. Acknowledgments This work was supported in part by GETA (Finnish Academy Graduate School on Electronics, Telecommunications and Automation), Finland.     

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.     

Specification and refinement of mobile systems in MTLA and mobile UML

We define the spatio-temporal logic MTLA as an extension of Lamport's Temporal Logic of Actions TLA for the specification, verification, and formal development of systems that rely on mobile code. The formalism is validated by an encoding of models written in the mobile UML notation. We identify refinement principles for mobile systems and justify refinements of mobile UML state machines with the help of the MTLA semantics.     

Online fault detection and isolation of nonlinear systems based on neurofuzzy networks

An online fault detection and isolation (FDI) technique for nonlinear systems based on neurofuzzy networks (NFN) is proposed in this paper. Two NFNs are used. The first one trained by data obtained under normal operating condition models the system and the second one trained online models the residuals. Fuzzy rules that are activated under fault free and faulty conditions are extracted from the second NFN and stored in the symptom vectors using a binary code. A fault database is then formed from these symptom vectors. When applying the proposed FDI technique, the NFN that models the residuals is updated recursively online, from which the symptom vector is obtained. By comparing this symptom vector with those in the fault database, faults are isolated. Further, the fuzzy rules obtained from the symptom vector can also provide linguistic information to experienced operators for identifying the faults. The implementation and performance of the proposed FDI technique is illustrated by simulation examples involving a two-tank water level control system under faulty conditions.