A parallel version of CYK- parsing **algorithm** for context – free grammar is presented by Chandwani, Puranik and Chaudhari[3, 4]. We present an **algorithm** which can store FRP in indexed array structure requiring simple parallel control constructs. We provide a detailed formulation and in- depth analysis of finding FRP using CYK-**algorithm** on parallel PRAM model of computation. In a rule-based expert system in which the knowledge is composed of a rule-base [5, 6, 18] an FDG can be used for graphical representation. An FDG is a graphically structured subset of a rule base R with rules of **fuzzy** propositions [7]. It can be used to perform automated **fuzzy** **reasoning**. A **fuzzy** **reasoning** path (FRP) can be established to define the antecedent-consequent relationship between source and goal propositions. One way to establish FRP is through **fuzzy** **logic**. In **fuzzy** **logic**, FRP defines an antecedent- consequent relationship of two propositions that leads to the greatest **fuzzy** value of the consequent proposition. The output of CYK **algorithm** is a pyramid that can be used to find the FRP. CYK **algorithm**‟s framework presented in this paper establishes the **fuzzy**

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A. The Basic Concepts of **Fuzzy** **Logic** Controller **Fuzzy** **logic** [2] is widely used in machine control. The term itself inspires a certain skepticism, sounding equivalent to ”half-baked **logic**” or ”bogus **logic**”, but the ”**fuzzy**” part does not refer to a lack of rigor in the method, rather to the fact that the **logic** involved can deal with **fuzzy** concepts - concepts that cannot be expressed as ”true” or ”false” but rather as ”partially true”. Although Genetic Algorithms can perform just as well as **fuzzy** **logic** in many cases, **fuzzy** **logic** has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans A **Fuzzy** **Logic** Controller (FLC) that is composed of the following: Knowledge base that includes the information given by the expert in the form of linguistic control rules; Fuzzification interface, which has the effect of transforming crisp data into **fuzzy** sets; Decision making unit, make the decision according to the knowledge base by using a **reasoning** method; Defuzzifi- cation interface, which produces a quantifiable result in **fuzzy** **logic** [8]. The general structure of an FLC is shown in Fig.1

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The soft computing techniques especially **fuzzy** **logic** has been used by many researchers for line tracing in mobile robots. A microprocessor-based **fuzzy** **logic** controlled line following robot is described by Reuss and Lee [2]. The robot is based on the RCX LEGO Mindstorms which incorporates an on-board Hitachi H8 microprocessor. Two light sensors are used under the robot to sense a white line drawn on a black surface and a **fuzzy** **logic** **algorithm** is used to move the robot to follow the line. A **fuzzy** **logic** controlled miniature LEGO robot for undergraduate training is described by Azlan et al., [3]. This study is divided into two parts. In first part, an object sorter robot is built to perform pick and place task to load different colored objects on a **fuzzy** **logic** controlled line following robot which then carries the preloaded objects to a goal by following the white line. In second part, **fuzzy** **logic** controlled light searching robot with the capability to navigate in a maze is developed. Harisha et al., [4] describes the design of a **fuzzy** **logic** **reasoning** system to control a mobile robot on predefined strip path with obstacles. The path guiding robot equipped with two IR sensors for line following and one IR proximity sensor for hurdle detection on path is able to navigate along strips with different speeds and stops when vehicle approaches to obstacle. A low cost educational microcontroller based tool for **fuzzy** **logic** controlled line following robot is described by Ibrahim and Alshanableh [5] which is used in the second year of undergraduate teaching in an elective course in the department of computer engineering of the Near East University. The robot is named as Robo-PICA and is equipped with a pair of infrared reflectors mounted at the bottom and at both corners of the robot. The designed **fuzzy** **logic** controller implemented inside PIC16F887 microcontroller using mikroC development environment keeps the robot on track. Another interesting paper on **fuzzy** **logic** and robot control is by Pawlikowski [6] where the development of a **fuzzy** **logic** speed and steering control system for an autonomous vehicle is described. Using an integrated vision system, the vehicle senses position relative to the angle of a line drawn on the ground, and processes that information through a **fuzzy** **logic** **algorithm**. The **algorithm** selects drive speeds for two independent motors, thereby providing the ability to go forward, or turn left or right while following a path.

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In this work, case-based **reasoning** is used to support the diagnosis and treatment of chronic diseases. Kushwaha and Welekar [52] used a data mining technique to develop an approach of feature selection for image retrieval based on content. The experimental result shows that the selection of features using Genetic **Algorithm** reduces the time for retrieval. Houeland [22] developed a RDT (random decision tree) **algorithm** implemented in a CBR framework. RDT **algorithm** has been combined with a simple similarity measure. They used two measures of similarity. A local subset of cases is selected using the first similarity measure. Then, the subset is reduced with the second measure of similarity by using a decision tree approach. RDT lets to define the similarity between two cases where trees are fully developed binary trees. The results have been evaluated in the area of palliative care of cancer.

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For **fuzzy** rule induction the FRL **algorithm** 1,12 was used as a basis. The **algorithm** constructs **fuzzy** clas- sification rules and can use nominal as well as nu- merical attributes. For the latter, it automatically extracts **fuzzy** intervals for selected attributes. One of the convenient features of this **algorithm** is that it only uses a subset of the available attributes for each rule, resulting in so-called free **fuzzy** rules. The KNIME implementation follows the published algo- rithm closely, allowing various algorithmic options to be set as well as different **fuzzy** norms. After ex- ecution, the output is a model description in a KN- IME internal format and a table holding the rules as **fuzzy** interval constraints on each attribute plus some additional statistics (number of covered pat- terns, spread, volume etc.). These KNIME repre-

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Abstract: Recently, many authors have been interested to introduce **fuzzy** implications over t-norms and t-conorms. In this paper, we introduce ( , ) S N and residuum **fuzzy** implication for Dubois t-norm and Hamacher's t-norm. Also, new concepts so-called ( , ) T N and residual **fuzzy** co-implication in dual Heyting Algebra are investigated. Some examples as well as application are discussed as well.

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In order to show how the WSN based system for continuous monitoring and/or recording critical temperature values, powered by **fuzzy** **logic** detection mechanism and web enablement, can be designed and deploy. The Internet of Things (IoT) is one of these methodologies which transform current Internet communication to Machine-to-Machine (M2M) basis. The IoT can seamlessly connect the real world and cyberspace via physical objects that embed with various types of intelligent sensors. A **Fuzzy** **Logic** System (FLS) is able to simultaneously handle numerical data and linguistic knowledge. It is a nonlinear mapping of an input data (feature) vector into a scalar output. **Fuzzy** set theory and **Fuzzy** **Logic** (FL) establish the specifics of the nonlinear mapping. Genetic Algorithms (GAs) are a family of computational models inspired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome-like data structure and apply recombination operators to these structures so as to preserve critical information. The GAs are often viewed as function optimizers, although the range of problems to which GAs have been applied is quite broad. An implementation of a GA begins with a population of (typically random) chromosomes. Then evaluates these structures and allocates reproductive opportunities in such a way that those chromosomes which represent a better solution to the target problem are given more chances to reproduce than those chromosomes which are poorer solutions.

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At the initial stage of CAFD two decades ago, The earliest research [12, 13] mainly developed the interactive CAFD systems, based upon an understanding of workpiece and process information. These systems usually provide fixture designers rules to evaluate the design and a list of components to be used interactively[14]. They mostly utilize expert systems as empirical device, which is often used to help fixture designer in interactive environments to achieve a complete fixture design solution especially for fixture configuration design [15]. In majority of these approaches, several IF-THEN rules are created based on design knowledge to lead the design process. Then, the solution of design in the interactive process of fixture design would be determined using created rules through a prearrange set of questioning-answering actions. Though, there are usually some difficulties for **reasoning** procedure mostly in case of constructing the **logic** tree and also sufficient comprehensive rule set which affect highly on the design result quality as well as efficiency of the design. Moreover, the **reasoning** procedure is also very boring in the interactive design modes.

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Abstract-- An agent-based negotiation platform for power generating and power consuming (purchasing) companies in contract electricity market is presented. An intelligent agent implements the negotiation process by selecting a strategy based on learning **algorithm** in an interactive manner with the user. Two kinds of learning **algorithm**---**fuzzy** **logic** controller modification of basic Genetic **Algorithm** for negotiation strategy optimization, and reinforced learning **algorithm** for negotiation tactics parameter modification---are provided for the agent. Protocol Operation Semantics that is flexible and can handle sequential message exchange is used as agent communication mechanism. The paper presents the architecture and negotiation strategy of agents. The software implementation of the platform is discussed in detail.

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ABSTRACT:This paper proposed an artificial intelligent control method for temperature control system and is suitable for low temperature applications such as laboratory equipments (e.g. ovens and incubators).The proposed design uses **fuzzy** PID as a control method that maintains the temperature of simulated heater to the desired point.The **Fuzzy** PID controller of heating system adds a conventional PID controller to the common **fuzzy** control and forms a mix **fuzzy** PID controller and it can realize the self adaptive of , and of PID.So **fuzzy** PID not only has the advantage of easy to application,strong robust,but also can be more widely used and have higher performance than normal PID. PID Microcontroller based circuit is built to acquire data from sensor, actuate heat element and communicate with computer workstation.As compared to normal PID the **Fuzzy**-PID **algorithm** has better performance of reducing temperature overshoot and improves systems performance.

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The Individuals which satisfy the fitness function has to perform the cross over and mutation. The cross over will be done by selecting the two individuals from the new population where the fitness value is checked, then the individuals will be cross over by any one of the methods. The cross over methods which can be done over the fitness selected individuals are single point cross over, double point cross over or multi point cross over. In our **algorithm** we have used multi point cross over. The cross over will randomly select the gene of the each individuals then swap the selected gene value and concatenated with each other, the resulted value will generate the new two individuals. The random selection of genes will be done by the roulette-wheel selection and based on the fitness value.

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projects have failed because of incorrect cost estimate as well as inadequate planning and timing. As a result, accurate estimation of software projects has taken on great significance [10][11][12]. Despite providing various methods for the estimation of effort in software projects, compatibility and accuracy of the existing methods is not yet satisfactory. Generally, the performance of the available approaches in different software projects is not the same and a wide range of performance discrepancies is evident. The present study provided a new approach increasing the estimation accuracy of software project effort. The proposed model is based on type-2 **fuzzy** **logic** applying two algorithms for training type-2 **fuzzy** system. As indicated by the literature, applying type-2 **fuzzy** **logic** can be helpful in effort estimation of software projects and increase the accuracy [1][7]. Section 2 of the present paper research instruments, section 3 presents the proposed model. In section 4, the evaluation parameters of the proposed model are introduced. Section 5 elaborates on the evaluation of the results, and conclusion is finally presented in section 6.

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FFA-DLB - **Fuzzy**-based Firefly **Algorithm** for Dynamic Load Balancing in Cloud Compu- ting [15]. It is a dynamic load balancing in Cloud computing environment and it is a combination of Firefly **algorithm** with the **fuzzy** **logic**, this **algorithm** separate the cloud based on the frequent node allocation to balance the load across the variety of partitions. The goal is to separate the hotspots and least loaded nodes, then classify the nodes into groups (like lightly loaded, normal, and heavily load- ed). The set of tasks enter into the load balancer after the partition of the cloud. This **algorithm** consid- er a balancing factor based on the parameters of the VM and the files to be processed from input. The **fuzzy** inference engine determine to assign the tasks, with a condition that already assigned tasks are migrated only when a high necessity arises.

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A prominent growth in a wireless communication system provides a wide range of services for a heterogeneous wireless network thus accomplishing user’s needs. These real time services are delay sensitive which requires a continuous internet connec- tion. Assuring a seamless end-end connectivity without link outages at the mid of an action is a crucial ongoing problem. Thus the main objective relies on designing a cognitive **algorithm** to achieve the practical impossibility of having a sustainable net connection while roaming across various technologies. Hence, to fulfill this and de- grade vertical handover (VHO) issues an enhanced **fuzzy** **logic** based **algorithm** is proposed. Further, it focuses on improving the transmission quality with low han- dover latency, less packet loss and reduced false handover. Along with this the algo- rithm achieves an accurate prediction in the decision making of an optimal network to which the idle node gets connected. The networking environment also has an effi- cient mobility management, where the functioning network controls the mobility of concerned mobile nodes. Finally the simulation process of this proposal on a com- prehensive test-bed infers the **fuzzy** **logic** based **algorithm**, guaranteed seamless con- nection. Further, the system has a 1.6% improvement in reducing the handover delay and 1.3% in packet loss reduction than the existing approaches, which in turn im- proves the transmission quality.

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Popular direction to rebuild big measure and great correctness in **fuzzy** **reasoning** maps to data model. The **fuzzy** modeling methods used to model **fuzzy** **logic** relations among changed notions in the procedure of bound for weighted graphs, wherever weights of edges represent the strength and type of relationships between two notions. The impartial knowledge of **fuzzy** **logic** is to find correct weights so that the reply of knowledgeable **fuzzy** is nearby to the experimental data as greatly as probable, which can be showed as an optimization problem. In the meantime the number of weights wants to be resolute growths with the number of notions; most existing **fuzzy** procedures can only handle problems with lots of nodes. However, the major network they treated has 30 inheritable factors. Therefore, more great education procedures that can handle important harms are necessary.

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We note that the original statement (p---+ q) happens to be true. We observe that the contrapositive is also true, but that the converse and inverse are false. Draw Venn diagrams to d[r]

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Type-2 **fuzzy** sets was introduced by Zadeh in 1975 [1] as an extension of the type-1 set. A type-2 **fuzzy** set is characterized by a concept called footprint of uncertainty (FOU).Consequently, the membership grade of each element in a type-2 **fuzzy** set is a **fuzzy** set in [0, 1], unlike a type-1 set where the membership grade is a crisp number in [0, 1]. **Fuzzy** **logic** systems (FLSs) constructed using type-2 **fuzzy** sets are type-2 FLSs to distinguish them from the traditional type-1 FLSs. Fig. 1 shows the schematic diagram of a type-2 FLS. Atype-reducer is needed toconvert the type-2 **fuzzy** output sets into type-1 sets before they are processed by the defuzzifier to give a crisp output. Since type-2 FLSs provide an extra mathematical dimension compared with type-1 FLSs, they are very useful in circumstances where it is difficult to determine an exact membership grade for a **fuzzy** set. Hence, they can be used to handle more system uncertainties and have the potential to outperform their type-1 counterparts. The FLCs have been applied to many areas , especially for the control of complex nonlinear systems that are difficult to model analytically [2]. Despite their popularity, research has shown that type-1 FLCs may have difficulties in modeling and minimizing the effect of uncertainties [3]. This limitation may restrict the usefulness of design methods that tune the FLCs using a Particle Swarm Optimization “PSO” algorithms and a model of the controlled process. Since it is impossible for a model to capture all the characteristics of the actual plant, the performance of a controller designed using a model will inevitably deteriorate when it is applied to the practical system. A controller takes fully into account the non-linear ties. Emerging intelligent techniques have been developed and extensively used to improve or to replace conventional control

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Input. Input signal 1is the position limit of the motion simulation platform (l), for the degree that the rotation angle of each joint of the five axis manipulator is close to the limit value, the rotation angle of each joint is close to the maximum value of the limit value as the input of the **fuzzy** controller. However, the range of motion of each joint is inconsistent. How to assess the extent of the approaching limit is worth quantifying. We can get the quantitative description function of the degree of near motion limit of each joint angle.

of radio resources facing users today since the available radio resources of a single RAT are still far from satisfying the requirements of increasingly mobile services [10]. The NGWN will provide significantly higher data rates, and offer a variety of services and applications. The NGWN is expected to allow a multimode mobile terminal to simultaneously access multiple networks for a variety of services with appropriate qualities of service and to choose the most suitable access network among the available access networks. In other words, a main feature of the NGWN is to provide always best connected services to users, that is, users can choose the best available access networks in a way that suit their needs, and to change to another best network if conditions change. Mobile users, including field workforces, can realize the goal of seamless communications and seamless always best connected service delivery through achieving the seamless mobility objective. Seamless communication involves the ability of the MT to successfully or simultaneously attach to different access points in the NGWN infrastructure in a way that makes the physical movement transparent and preserves application-level connectivity unaltered. The NGWN mobile users will enjoy seamless mobility and ubiquitous access to applications in an always best connected (ABC) mode that employs the most efficient combination of available access systems. The goal of the mobile user is to be always best connected, that is, to be not only connected, but also connected through the best available device and access technology at all times [11]. Seamless mobility and seamless service continuity can be achieved by enabling multimode mobile terminals to conduct seamless handoffs with low latency and minimal packet loss across heterogeneous wireless access networks, for example, across mobile WiMAX and UMTS access networks, seamlessly transferring and continuing their ongoing sessions from one access network to the best available target access network. A handoff **algorithm** is required to preserve connectivity as the mobile terminals move about, and at the same time curtail disturbance to ongoing transfers. Consequently, seamless handoff, with low latency and minimal packet loss, has become a crucial factor for field workforces and other mobile users who wish to receive continuous and reliable services. One of the challenging issues in the multi-service NGWN is to design intelligent and optimal vertical handoff decision algorithms, beyond traditional ones that are based on only signal strength, to determine when to perform a handoff and to provide an

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