Genetic, Radial basis and GRNN algorithm with 19 distinct features. Genetic algorithm considered to be best with 96.77% classification accuracy for diagnosing breast cancer [14]. Sizilio G et al. in 2012 proposed a model for pre-diagnosis breast cancer with Finite Needle Aspirate (FCA) approach implemented in MATLAB. The accuracy of the system was measured as Sensitivity 98.59% and Specificity as 85.43% [15]. Another disease classification was done by Sagir A.M in 2017 paid attention to deal with breast cancer by implementing ANFIS, Modified LM and Gradient Descent algorithms and proved 84% accurate via **fuzzy** **logic** toolbox [16]. Different methodologies and findings in brief represented in Table 4 as follows.

otherwise; the type-1 FLC performance might deteriorate (Mendel, 2001). As a consequence, research has started to focus on the possibilities of higher order FLCs, such as type-2 FLCs that use type-2 **fuzzy** sets. A type-2 **fuzzy** set is characterized by a **fuzzy** MF, that is, the membership value (or membership grade) for each element of this set is a **fuzzy** set in [0, 1], unlike a type-1 **fuzzy** set where the membership grade is a crisp number in [0,1] (Hagras, 2004). The MF of a type-2 **fuzzy** set is three dimensional and includes a footprint of uncertainty. It is the third dimension of the type-2 **fuzzy** sets and the footprint of uncertainty that provide additional degrees of freedom making it possible to better model and handle uncertainties as compared to type-1 **fuzzy** sets. In this paper, adaptive network based **fuzzy** **inference** system (ANFIS) was used as interval type-2 **fuzzy** **logic** controller (IT-2FL) in control strategies of the Heat Exchanger. Interval type2 **fuzzy** **logic** control was not taken into consideration by this approach in most of the cited investigations, despite some of its advantages indicated in this study. Proposed type-2 **fuzzy** **logic** controller combines two different control techniques which are adaptive network based **fuzzy** **logic** **inference** system control and interval type-2 **fuzzy** **logic** control, and uses their control performances together. Adaptive network based **fuzzy** **inference** system (ANFIS) uses a hybrid learning algorithm to identify parameters of Sugeno-type **fuzzy** **inference** systems. A combination of the least squares method and the back propagation gradient descent method is used in training interval type2 **fuzzy** **inference** system (IT2FIS) membership function parameters to emulate a given training data set. Moreover MATLAB/Sim-Mechanics toolbox and computer aided design program (Solid Works) was used together for visual simulations.. Also MATLAB/ANFIS toolbox was used to create adaptive network based **fuzzy** **logic** **inference** system controllers.

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The decision of the motion is based on the conventional **fuzzy** **logic** **inference** stages shown in Fig.3. The goal of the **fuzzy** **logic** controller is to enable the mobile robot to represent the target motion attributes, namely the image displacement and image velocity of the target object. We design a FLC to control the mobile robot steering commands. The output of the controller is responsible for steering the vehicle such that the target object appears continually in the middle of the image.

Abstract: This study identifies the risk factors of recurrent tonsillitis in pediatric patient which in turn are the variables used in developing a predictive model for predicting the risk of recurrent tonsillitis. This is achieved by eliciting knowledge on the risk factors of recurrent tonsillitis, formulating the model using the variables and simulating the model using MATLAB tool. Interviews were conducted with the pediatrician and existing literature was studied on the knowledge of study in order to identify the variables for recurrent tonsillitis. Seven (7) data from tonsillitis patients were collected from Wesley Guild Hospital, Ilesha. Predictive model was formulated using the **fuzzy** **logic** model and simulated on MATLAB R2016a. **Fuzzy** **logic** was used as the predictive model to determine the risk of recurrent tonsillitis. The stages involved in the process are four (4) which includes: fuzzification, rule production, aggregation and defuzzification. The identified variables were given crisp values and within a membership function of 0 and 1. The simulated result of the **fuzzy** **logic** model was done using MATLAB which involved formulation of the **fuzzy** **logic** **inference** system (FIS) which was carried out by the MATLAB tool. The variables which are the risk factors were used to build the **fuzzy** **logic** **inference** system (FIS) to determine the risk of recurrent tonsillitis. Possible combinations of rules were given for the variables and the rules were used in the **inference** engine to predict the output of the model whether it is no, low, moderate or high risk of recurrent tonsillitis. The validation was done on the data gotten from Wesley Guild Hospital Ilesha from 7 patients. In conclusion, out of the seven (7) patients test data provided, five (5) patients have low risk, two (2) patients have moderate risk, no patients have no low risk and no patients have high risk of recurrent tonsillitis with 100% test accuracy.

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plant with the application of a neuro-**fuzzy** model [5]. The input parameters viz. effective frictional force between the surfaces of the material and the walls of the extrusion chamber and diameter of the extrusion chamber determine the rate of flow of the solid waste material to be recycled through the extrusion chamber. The model is designed such that the most favorable condition where maximum quantity of solid waste material is recycled is attained. The linguistic variable serves as the engine of the model in bringing about relationship between the input and the output parameters to evaluate the outcome of such relationship. The result obtained indicates the feasibility of applying the neuro-**fuzzy** model in plastic recycling extruder process.

Cyber security and intrusion detection has emerged as a significant field of research, because it is not theoretically possible to set up a complete system with no fault. Intrusion incidents to computer systems are increasing because of the widespread usage of the internet and local networks. It is known that different machine learning algorithms, for example support vector machine, genetic algorithm, neural network , data mining, **fuzzy** **logic** and some others have been extensively applied to detect intrusion activities.

ABSTRACT: In this paper, adaptive network based **fuzzy** **inference** system (ANFIS) was used in control applications of a Shell and Tube Heat Exchanger as interval type-2 **fuzzy** **logic** controller (IT-2FL). Two adaptive networks based **fuzzy** **inference** systems were chosen to design type-2 **fuzzy** **logic** controllers for each control applications. The whole integrated system for control of Shell and Tube Heat Exchanger is called IT2 FLC+ANFIS controller. Membership functions in interval type-2 **fuzzy** **logic** controllers were set as an area called footprint of uncertainty (FOU), which is limited by two membership functions of adaptive network based **fuzzy** **inference** systems; they were upper membership function (UMF) and lower membership function (LMF). This paper deals with the design and application of an IT2 FLC+ANFIS controller for a Shell and Tube heat exchanger. The IT2 FLC+ANFIS controller of the heat exchanger is compared with classical PID control. System behaviors were defined by Lagrange formulation and MATLAB computer simulations. The simulation results confirm that interval type2 **fuzzy** is one of the promises for successful control of heat exchangers. The advantage of this approach is that it is not a linear-model-based strategy. Comparison of the simulation results obtained using IT2 FLC+ANFIS controller and those obtained using classical PID control demonstrates the effectiveness and proves its simplicity and superiority over the conventional PID controller.

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Abstract : This paper deals with **Fuzzy** **logic** an idea which is easy to understand. **Fuzzy** **logic** provides an alternative way to represent linguistic and subjective attributes of the real world in computing. The cause for selection of **fuzzy** **logic** model in this revision is that the system uses **fuzzy** **logic** model enables to provide useful results depending on uncertain verbal knowledge just like **logic** of human being. The value of **fuzzy** **logic** model usage here is to reach a general solution by doing only incomplete experiments. It takes long time to use the other methods for such problem. The **fuzzy** **logic** provides the quickest way out to the problem prevents to lose. It is a outline of multiple-valued **logic** which has other than two truth values. It uses the concept of level of membership. In Boolean **logic**, the truth standards may be only 0 or 1, but in **fuzzy** **logic**, they will be any real number between 0 and 1 i.e. the truth values will vary between true and false. Fuzzification which comprises of the development of transforming hard values into grades of membership for linguistic terms of **fuzzy** sets and **Fuzzy** set is a set that allows its members to have various degrees of membership within 0 and 1 i.e. within true and false. **Fuzzy** system is based on a logical system which is much closer to human thinking and natural language.

The process of **fuzzy** **logic** was explained in Algorithm: Firstly, a crisp set of input data are gathered and converted to a **fuzzy** set using **fuzzy** linguistic variables, **fuzzy** linguistic terms and membership functions. This step is known as fuzzification. Afterwards, an **inference** wass made based on a set of rules. Lastly, the resulting **fuzzy** output was mapped to a crisp output using the membership functions, in the defuzzification step.

dataset is a time series that shows the number of lynx trapped in the Mckenzie river district per year in northern Canada and corresponds to the period 1821-1934. Similar to previous studies such as [26]–[28], the logarithms to the base 10 of the data are used in the analysis. Figures 4 and 5 show the original and the logarithmic transformed data of the Canadian lynx series respectively, with a periodicity of approximately 10 years. The series consists of 114 observations of which 100 samples are used for training and the remaining 14 are used for testing in order to validate the effectiveness of the model proposed in this study. Similar to [28], the maximum training epoch adopted is 2000. As shown in Table III, IT2IFLS outperforms the listed non-**fuzzy** approaches on the Canadian lynx dataset.

In most cases, knowledge about the optimisation problem does not exists explicitly therefore we often define the objectives in a qualitative manor such as ―It is more important that the UAV does not fly into a wall rather than landing precisely on the helipad.‖ However this presents a problem for stochastic/deterministic systems as there is no precise mathematical description to quantify this information. Using **fuzzy** subsets and **inference** rules, the Importance factor Ii and the value of improvement Di(ap,aq)= Qi(s, ap) − Qi(s, aq) are used as inputs to construct the action selection mechanism (Humphrys, M. 1996). Another example using the AHP algorithm is the Greenhouse Parameter Control Strategy demonstrated in Qian and Wang (2013) where the target for the RL agent is to produce as high yield of crops as possible by taking into account several environmental factors such as light intensity, Co2 level, humidity, and temperature. Various equipment such as heaters, ventilation fans, irrigation equipment and LED lights are controlled by the AHP algorithm to provide a more scientific and reasonable sequence in selecting control measures. Providing the ideal growth values for any particular crop are known, this method may be used to automatically collect and control crop growth parameters to be used as an automatic control algorithm for maximising any crops production yield.

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**Fuzzy** **logic** is an effective paradigm to handle imprecision. It can be used to take **fuzzy** or approximate observations for inputs and yet arrive at crisp and precise values for outputs. Also, the FIS is a simple and rational way to build systems without using complex analytical equations. In our example, **fuzzy** **logic** will be employed to capture the broad categories identified during clustering into a FIS. The FIS will then act as a model that will reflect the relationship between sensor observations and predicted month of the year. Clustering and **fuzzy** **logic** together provide a simple yet powerful means to model the relationship between sensor and physical oceanic parameters that we want to study. Figure 7 represents the schematic diagram of the whole Sugeno-type FCM-FIS system that has been used in this study. FCM based subtractive clustering estimates the cluster centers in a set of data by using the subtractive clustering method. The subtractive clustering method assumes each data point is a potential cluster center and calculates a measure of the likelihood that each data point would define the cluster center, based on the density of surrounding data points. The cluster's radius of influence in the input space was set to 3.5 for justification against significant amount of overlapping among the clusters.

“While traditional or ‘hard’ computing uses crisp values, or numbers, soft computing deals with soft values, or **fuzzy** sets. Soft computing is capable of operating with uncertain, imprecise and incomplete information in a manner that reflects human thinking. In real life, humans normally use soft data represented by words rather than numbers. Our sensory organs deal with soft information, our brain makes soft associations and inferences in uncertain and imprecise environments, and we have a remarkable ability to reason and make decisions without using numbers. Humans use words, and soft computing attempts to model our sense of words in decision making [12].

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**Fuzzy** adaptive Smith predictor was used to control three-tank-system with delay in this paper. The **fuzzy** adaptive PID part can improve the resisting ability and adaptive ability of the system to random disturbance, and the Smith predict control part can overcome the delay characteristic of the controlled object. MATLAB7.4.0 is used as development platform and three-tank-system as research object in this paper. Smith — PI method, **fuzzy** adaptive PID method, and **fuzzy** adaptive Smith —PID method were used in the simulation. The simulation results show that **fuzzy** adaptive Smith—PID method can make the system has better adaptive ability, shorter settling time, better stability, and stronger anti-interference ability.

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Copyright to IJAREEIE www.ijareeie.com 12815 The ANFIS is used to implement the proposed model .It uses the collected training data to train the **fuzzy** detection system which have 4 inputs with one output. Using a given input/output data set, [10] the toolbox function ANFIS constructs a **fuzzy** **inference** system (FIS) whose membership function parameters are tuned (adjusted) using either a back propagation algorithm alone or in combination with a least squares type of method . [11]

B. Implementation of IVCIM using **fuzzy** controller The simulink implementation of **fuzzy** **logic** based IVCIM drive is shown in Figure-7. Figure-5 illustrate the membership functions used for error and variation in errorinputs. Figure-6 shows the membership function for output of the **fuzzy** **logic** controller. The **fuzzy** controller is established to process the speed error and derivative of error as two input membership role and

output variable of the **fuzzy** controller the MFs used for input and output **fuzzy** sets are shown in Fig.6 and in the next stage, after computing the inputs through knowledge base and inferencing mechanism is that of de-fuzzification. Here in this paper the deffuzification technique chosen is the centre of gravity. Then, the control output U fN can be calculated as follows:

Abstract: With the widespread use of large-capacity power electronic devices, there are too much harmonic current and harmonic voltage in power system at the same time. The active power filter (APF) or hybrid active power filter (HAPF) can only eliminate harmonic current or harmonic voltage, and possibility resonance. To eliminate harmonic current and harmonic voltage in power system simultaneously and enhance the capacity, the concept presents a hybrid unified power quality conditioner (HUPQC). Compared with UPQC, the HUPQC is made up of a hybrid series active power filter (the series device) and a shunt active power filter (the shunt device). The shunt device employs an injection circuit to lower the capacity of the active part to fit high-voltage power system. Designed reasonably, and controlled with composite control method, the HUPQC can filter the harmonic current and harmonic voltage effectively at the same time. At the end of this concept, the simulation results improve that the HUPQC can prove the power quality greatly and achieve satisfactory effect.The proposed concept is further implemented to **Fuzzy** controlled concept using Matlab/Simulink software

Many stock data have been analyzed to compare the adaptive system by using the Amibroker and Matlab software package. The analysed result claims that the signal and result processed by adaptive **fuzzy** rule based scheme agrees with the result of technical indicators RSI, Stochastic [4,9-11]. It can be claimed that trading system with Adaptive **fuzzy** rule based scheme will provide satisfactory result. Automated trading system with adaptive **fuzzy** rule based scheme would have been a demanding toolkit for the investors. Acknowledgement. The authors would like to thank for the cooperation of Prof. Dr. M. Dilder Hossain and Prof. Dr. Jahanara Begum Department of Basic Science, Primeasia University, Banani, Dhaka.

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Road intersections, bad roads, accidents, road construction works, emergencies, etc. are some of the primary causes of high traffic congestions in urban areas. In an attempt to solve some of these problems, traffic wardens and traffic light control systems are employed at road intersections to ensure that deadlocks are avoided. However, the use of traffic warden is associated with weariness which can lead to poor judgement in allocating the right of way to motorist. An alternative approach is to employ the use of Traffic light control system in the management of the increased traffic congestion that is always experience in urban areas. The use of dynamic phase scheduling traffic control system has proven more efficient as compared to the static phase scheduling traffic control system. In this paper, an attempt was made to improve upon an earlier optimized traffic light control system developed using simulation of urban mobility (SUMO) in conjunction with **fuzzy** **inference** system which played the role of optimizing the traffic light control system. The modified **fuzzy** rule based gave a superior average waiting time of 72.07% improvement as compared to an earlier average waiting time improvement of 65.35%. This is an indication that amongst other factors, the size of the **fuzzy** rule base plays a significant role when **fuzzy** **logic** is employed in the optimization of traffic light control systems.