As the electricity rates during peak hours are higher, this paper proposes a design for an ultrabook to automatically shift the charging period to an off-peak period. In addition, this design sets an upper limit for the battery which thus protects the battery and prevents it from remaining in a continued state of both high temperature and high voltage. This design uses both a low-power embedded controller (EC) and the fuzzy logic controller (FLC) control method as the main control techniques together with real time clock (RTC) ICs. The sensing value of the EC and the presetting of parameters are used to control the conversion of the AC/DC module. This user interface design allows the user to set not only the peak/off-peak period but also the upper use limit of the battery.

This paper presents an approach to implementing simulation models for SAM fuzzy controllers without the use of external components. The approach represents a fuzzy controller as a composition of simple simulation models which involve only basic operations.

This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

Because of the fact that mobile communication channel changes by time, it is necessary to employ adaptive channel equalizers in order to combat the distorting effects of the channel. Least Mean Squares (LMS) algorithm is one of the most popular channel equalization algorithms and is preferred over other algorithms such as the Recursive Least Squares (RLS) and Maximum Likelihood Sequence Estimation (MLSE) when simplicity is the dominant decision factor. However, LMS algorithm suffers from poor performance and convergence speed within the training period specified by most of the standards. The aim of this study is to improve the convergence speed and performance of the LMS algorithm by adjusting the step size using fuzzy logic. The proposed method is compared with the Channel Matched Filter-Decision Feedback Equalizer (CMF-DFE) [1] which provides multi path propagation diversity by collecting the energy in the channel, Minimum Mean Square Error-Decision Feedback Equalizer (MMSE-DFE) [2] which is one of the most successful equalizers for the data packet transmission, normalized LMS-DFE (N-LMS-DFE) [3] , variable step size (VSS) LMS-DFE [4] , fuzzy LMS-DFE [5],[6] and RLS-DFE [7] . The obtained simulation results using HIPERLAN/1 standards have demonstrated that the proposed LMS-DFE algorithm based on fuzzy logic has considerably better performance than others.

RFID event filtering is an important issue of RFID data management. Tag read events from readers have some problems like unreliability, redundancy, and disordering of tag readings. Duplicated events lead to performance degradation of RFID systems with a flood of similar tag information. Therefore, this paper proposes a fuzzy logic-based quantized event filter. In order to reduce duplicated tag readings and solve disordering of tag readings, the filter applies a fuzzy logic system to control a filtering threshold by the change in circumstances of readers. Continuous tag readings are converted into discrete values for event generation by the filtering threshold. And, the filter generates as many events as the discrete values at a point of event generation time. Experimental results comparing the proposed filter with existing RFID event filters, such as the primitive event filter and the smoothing event filter, verify effectiveness and efficiency of the fuzzy logic-based quantized event filter.

A fuzzy logic control battery equalizing controller (FLC-BEC) is adopted to control the cell voltage balancing process for a series connected Li-ion battery string. The proposed individual cell equalizer (ICE) is based on the bidirectional Cuk converter operated in the discontinuous capacitor voltage mode (DCVM) to reduce the switching loss and improve equalization efficiency. The ICE with the proposed FLC-BEC can reduce the equalizing time, maintain safe operations during the charge/discharge state and increase the battery string capacity.

The purpose of this paper is to present a case study of the development, implementation and performance analysis of an autonomous flight control strategy for a 1-meter small-sized unmanned aerial vehicle. Firstly, a learning algorithm based open-loop control is proposed by simulating a skilled human operator's manipulation of the aircraft. This is aimed to generate a set of command data inputs and investigate the multi-channel control characteristics with the open-loop control. Secondly, a feedforward plus a proportional and derivative (PD) feedback control is employed to control the vehicle in following the command data to complete the loitering flight. The PD control gains are tuned automatically according to the attitude of the vehicle using the fuzzy logic theory. Thirdly, autonomous flight experiments conducted on a 1-meter small-sized aerial vehicle demonstrated the effectiveness of the proposed method.

This paper deals with new fuzzy controller for handling systems having large dead time and nonlinearities, via approximations of large rule fuzzy logic controller by simplest fuzzy controller (4 rules). The error between large rule fuzzy controller and simplest fuzzy controller are compensated by proposed compensating factors. These compensating factors are modified to handle large dead time and nonlinear systems. Features of proposed approximations are discussed. The concept of variation of nonlinearity factor of fuzzy controller is also discussed. Various processes from different literatures are utilized to demonstrate the proposed methodology. After doing many simulations it has been found that with proper tuning, overall system handles large dead time and nonlinearity which may be difficult by conventional methods. The processes are also simulated for load disturbances and change of operating point (set point) and it has been found that proposed scheme is robust for long dead times.

Wireless local area networks, in which every station can transmit via any one of the available operating channels, but only one channel at a time, are investigated. Two distributed random access protocols are proposed for these WLANs. The CSMA/CA protocol is similar to the IEEE 802.11 standard but with slight modifications for multiple operating channels. The fuzzy logic controlled protocol employs a simple fuzzy logic controller to tune the size of backoff window. Extensive simulations are provided to evaluate the channel utilization, fairness, and responsiveness of these two protocols. Furthermore, the effects of employing RTS/CTS mechanism with both protocols are considered. Finally, performances of these two protocols are also investigated under conditions of burst traffic and noisy channels. Simulation results show that the fuzzy logic controlled protocol is a great improvement of the CSMA/CA protocol.

Correct and quick generation of a membership function is the key point when we implement a real-time fuzzy logic controller. In this Letter, we presented two efficient VLSI architectures, one to generate triangle-shaped and the other to generate trapezoid-shaped membership functions. Simulation results show that our designs require lower hardware cost but achieve faster working rate.

The redundancy allocation problem for a series-parallel system is a well known as one of NP-hard combinatorial problems and it generally belongs to the class of nonlinear integer programming (nIP) problem. Many researchers have developed the various methods which can be roughly categorized into exact solution methods, approximate methods, and heuristic methods. Though each method has both advantages and disadvantage, the heuristic methods have been received much attention since other methods involve more computation effort and usually require larger computer memory. Genetic algorithm (GA) as one of heuristic optimization techniques is a robust evolutionary optimization search technique with very few restrictions concerning with the various design problems. However, GAs cannot guarantee the optimality and sometimes can suffer from the premature convergence situation of its solution, because it has some unknown parameters and it neither uses a priori knowledge nor exploits the local search information. To improve these problems in GA, this paper proposes an effective hybrid genetic algorithm based on, 1) fuzzy logic controller (FLC) to automatically regulate GA parameters and 2) incorporation of the iterative hill climbing method to perform local exploitation around the near optimum solution for solving redundancy allocation problem. The effectiveness of this proposed method is demonstrated by comparison results with other conventional methods on two different types of redundancy allocation problems.

In this study, a hybrid genetic algorithm/neural network with fuzzy logic controller (NN-flcGA) is proposed to find the global optimum of reliability assignment/redundant allocation problems which should be simultaneously determined two different types of decision variables. Several researchers have obtained acceptable and satisfactory results using genetic algorithms for optimal reliability assignment/redundant allocation problems during the past decade. For large-size problems, however, genetic algorithms have to enumerate numerous feasible solutions due to the broad continuous search space. Recently, a hybridized GA combined with a neural network technique (NN-hGA) has been proposed to overcome this kind of difficulty. Unfortunately, it requires a high computational cost though NN-hGA leads to a robuster and steadier global optimum irrespective of the various initial conditions of the problems. The efficacy and efficiency of the NN-flcGA is demonstrated by comparing its results with those of other traditional methods in numerical experiments. The essential features of NN-flcGA namely, 1) its combination with a neural network (NN) technique to devise initial values for the GA, 2) its application of the concept of a fuzzy logic controller when tuning strategy GA parameters dynamically, and 3) its incorporation of the revised simplex search method, make it possible not only to improve the quality of solutions but also to reduce computational cost.

In Asynchronous Transfer Mode (ATM) networks, congestion can be caused by unpredictable statistical fluctuations of traffic flows and fault conditions within the network. If congestion happens, then the network performance for the already established connection will decrease. ATM networks use the preventive congestion control mechanisms such as Usage Parameter Control (UPC) and Connection Admission Control (CAC) to avoid congested conditions. Knowing that many sources in ATM networks have variable traffic stream with different QoS characteristics, traffic management functions become necessary to control the traffic flows within the network. By using the signaling procedures at the call setup phase, the network and source reach an agreement for some traffic characteristic parameters. If the source violates the traffic parameters, then the probability of congestion increases. So the network must control the source traffic streams and detect well the violating cells. Therefore, fast detection of any violating source is one of the most important characteristics of a good traffic policer. In this paper we propose a fuzzy traffic policer with high ability in detection of violating sources. Our proposed fuzzy controller has two inputs, estimated passed mean cell rate and the current state of the counter. If the output of fuzzy controller is 1, then the input cell is detected as violating cell, otherwise it is a non-violating cell. Simulation results obtained from two traffic sources, show that the proposed traffic policer has better selectivity than the conventional leaky bucket. It is observed that our proposed traffic policer has better ability for mean cell rate control, peak cell rate control and burst duration control. Furthermore, it is observed that the proposed traffic policer outperforms the conventional leaky bucket specially when the dynamic behavior is considered.

In this paper, we propose an efficient and stable algorithm employing fuzzy logic for available bit rate (ABR) traffic. Large time-delay incurred in the feedback path and the statistical variation in the link capacity at the ATM node can cause instability or unfairness. The stability and fairness issues are discussed and the performance of the proposed mechanism is evaluated.

This paper is concerned with the problem of (exactly) representing given functions by fuzzy reasoning. We consider function representation by the fuzzy reasoning method using linguistic truth values, which is a generalization of fuzzy reasoning due to Zadeh. Some conditions for functions to be representable are given, by which it is shown that very large class of functions can be representable by this method. Some examples illustrating how to find "if-then rules" for fuzzy reasoning are shown. Further, in the appendix an example is given to show that the generalization is significant for the problem of function representation.