CHAPTER 3 TWO LEVEL FUZZY LOGIC BASED INTRUSION DETECTION SYSTEM (TLFL IDS) FOR BLACKHOLE ATTACKS IN WIRELESS AD HOC NETWORKS 3.1 FUZZY LOGIC 3.1.1 INTRODUCTION TO FUZZY LOGIC A fuzzy logic system (FLS) can be defined as the nonlinear mapping of an input data set to a scalar output data .A FLS consists of four main parts namely fuzzifier, rules, inference engine and defuzzifier. The fuzzy logic process has been explained by the following steps. Initially, a crisp set of input data have been converted into a fuzzy set by using fuzzy linguistic variables, fuzzy linguistic terms and membership functions. This process is known as fuzzification. Secondly an inference is framed by using a set of rules.
Fuzzy logic is mostly applied in the field of control systems as they can interpret human actions. This controller’s use decision making rules like if-then on critical variables to interpolate the output between crisp boundaries. Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership. Fuzzy logic is an approach where human reasoning is imparted in the control
To compare the simulation performance of the PID controllers, LQR, Fuzzy controllers on the cart-inverted pendulum. To apply the developed controllers on a real cart-inverted pendulum To use the developed controllers in a real time application 1.4 Methodology This is the step by step approach taken in order to realize the objective of this project. The approach is as follows. • Background reading of existing work related to the control of nonlinear systems- It entails looking at what other people have done in regards to the control of nonlinear systems and analyzing their findings. This will help me to narrow to some few controllers with better results that be used in a real time application.
To attain stable hovering, the set point for each controller should be zero, a state in which the quadcopter is ideally leveled to the surface. For moving, the set point is regulated up or down making the quadcopter tilt in the desired direction of motion. For real time applications, each PID controller computes the adjacency of the prevailing control input with the desired output, the rate at which the output is advancing the set point, along with the time duration for which the input drifted from the set point. These computations would respectively make up the proportional, integral, and derivative elements of the control
Firstly a classical PID controller is implemented to achieve the control objectives. PID controller exhibits high overshoots which is undesirable. To minimize the overshoot FOPID controller is implemented. FOPID based controller has gained widespread acceptance because of its robustness; it has five parameters to tune instead of three. In this paper three types of controller are designed to achieve the control objective and a comparative study between the controllers are evaluated
Fig.4.1 shows the general block diagram of the PID controller. The main advantages of this controller are oscillation free, higher stability and fast response. The united operation of proportional, integral and derivative control gives the control strategy for process control. It continuously calculates an error value e(t) as the variation between a preferred set point and a calculated variable and applies a rectification based on proportional, integral and derivative terms. In this controller has much attention required for selecting finest values of proportional, integral and derivative gains.
The vertical component of the wind offsets the sink rate of the bird or aircraft, which can be obtained easily from the wind blowing up a slope. Many bird species are observed to use this slope soaring in proximity to ridges and cliffs . RC pilots also make use of this slope soaring frequently. Collectively, thermal soaring and slope soaring come under the category static soaring, as steady vertical component of the wind is used in obtaining the energy. Energy extraction from high frequency turbulence in the atmosphere is known as gust soaring .
One of the difficult challenges of the application of fuzzy logic in spatial earth science disciplines is how to assign fuzzy membership values of conditioning factors (Bui et al. 2014). In this context, different methods have been applied to address this issue. For examples, techniques such as Cosine Amplitude (Kanungo et al. 2009l; Rather & Andrabi, 2012), frequency
INTRODUCTION As of late, the number and assortment of utilizations of fuzzy logic have expanded fundamentally. The applications run from buyer items, for example, cameras, camcorders, clothes washers, and microwave stoves to modern process control, restorative instrumentation, choice emotionally supportive networks, and portfolio determination. To comprehend why utilization of fuzzy logic has developed, you should first comprehend what is implied by fuzzy logic. Fuzzy logic has two distinct implications. In a restricted sense, fuzzy logic is a legitimate framework, which is an augmentation of multivalued rationale.