fuzzy logic-AMkkkkkkkkkkkkkkkkkkkkk.pptx

Fuzzy logic is explained very well..

1. Data Mining By: Asad Khan Lecturer at GPGC Lakki Marwat

2. Fuzzy Logic The term fuzzy refers to things that are not clear or are vague. In the real world many times we encounter a situation when we can’t determine whether the state is true or false, their fuzzy logic provides very valuable flexibility for reasoning. In this way, we can consider the inaccuracies and uncertainties of any situation. Fuzzy Logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1, instead of just the traditional values of true or false. Fuzzy Logic is based on the idea that in many cases, the concept of true or false is too restrictive, and that there are many shades of gray in between. Fuzzy Logic is used in a wide range of applications, such as control systems, image processing, natural language processing, medical diagnosis, and artificial intelligence.

3. Cont… In summary, Fuzzy Logic is a mathematical method for representing vagueness and uncertainty in decision-making, it allows for partial truths, and it is used in a wide range of applications. It is based on the concept of membership function and the implementation is done using Fuzzy rules. In the boolean system truth value, 1.0 represents the absolute truth value 0.0 represents the absolute false value. But in the fuzzy system, there is no logic for the absolute truth and absolute false value. But in fuzzy logic, there is an intermediate value too present which is partially true and partially false.

4. How Does Fuzzy Logic Work? Fuzzy Logic is highly beneficial for simulating human thought in a controlled ecosystem. It is more concerned with acceptable logic, which closely resembles how the actual world functions. It is intended to handle uncertainty and is adept at drawing conclusions from the result. To use fuzzy logic, one needs to follow these steps: Select what you are trying to control: The user must understand what he is attempting to manage, the needed reaction, potential system flaws, and how to operate the system. Identify the relationship between input, output, and potential variations and error/exception: Users must evaluate the connection between outputs and inputs and choose the minimal number of input variables and error rate for fuzzy logic.

5. Cont… Create the if/then rule: By using the rule-based architecture of fuzzy logic, firstly decompose the control issue into a sequence of “IF A AND B THEN C“ rules that specify the intended response under specified situations. The rule’s complexity is determined by the number of inputs and variables connected with each input. Write the code function : Create a membership function for fuzzy logic that defines the weighted value of input and output elements. Outline the cycle of operations: Create a method for fuzzy logic before and after its hardware or software implementation. Test your systems : System testing and result evaluation enable you to fine-tune membership functions as well as rules. Repeatedly test and adjust to achieve the desired outcomes .

6. Applications of Fuzzy Logic: Control Systems : Fuzzy logic is widely used in control systems, such as air conditioners, washing machines, and car anti-lock braking systems (ABS), where it can handle the nuances of varying conditions and provide smooth control without complex mathematical models. Decision-Making : Fuzzy logic is applied in decision-making systems where decisions need to be made based on imprecise or ambiguous information, such as in medical diagnosis or financial forecasting. Pattern Recognition : Fuzzy logic is used in pattern recognition tasks, such as handwriting recognition, where the patterns are not strictly defined and can vary significantly. Expert Systems : Fuzzy logic helps in developing expert systems that emulate human reasoning, allowing the system to make decisions in uncertain or complex environments. Image Processing : Fuzzy logic is employed in image processing to enhance, segment, or classify images based on vague or fuzzy criteria.

7. What is a fuzzy set? Fuzzy set theory, introduced by Lotfi A. Zadeh , redefines the conventional notion of set theory by accommodating the granularity of membership within a set. Unlike classical set theory, which employs binary membership functions, fuzzy sets allow for a continuum of membership grades, thereby enabling the representation of partial truths . In the context of artificial intelligence, fuzzy set is a fundamental construct that facilitates the modeling of vague and ambiguous data, reflecting the intricacies of human cognition and linguistic expressions. It contrasts with crisp sets by encapsulating the notion of gradual transition between membership and non-membership, offering a more sophisticated approach to handling uncertainty.

8. How fuzzy set works Fundamentally , a fuzzy set is characterized by its membership function, which assigns a degree of membership to each element within the set. The critical feature of fuzzy set theory is its ability to capture the gradual transition from full membership to non-membership, allowing for a more expressive representation of imprecise concepts and information . The calculation of the degree of membership in a fuzzy set is driven by the concept of fuzzy logic, which facilitates the incorporation of uncertainty into decision-making processes. Through the formulation of membership functions, fuzzy set theory enables AI systems to process and interpret vague or ambiguous data, effectively navigating scenarios where traditional binary classifications fall short.

9. Operations on Fuzzy Sets: Union (OR operation): The degree of membership of an element in the union of two fuzzy sets and and is given by the maximum of its membership values in the two sets: Intersection : Fuzzy set D~ is intersection of Fuzzy sets A~ and B~ : Complement : Fuzzy set E~ is complement of Fuzzy set A~ :  

10. Artificial Neural Networks Artificial Neural Network (ANN) is a computational model inspired by the human brain’s neural structure. It consists of interconnected nodes (artificial neurons) organized into layers. Information flows through these nodes, and the network adjusts the connection strengths (weights) during training to learn from data, enabling it to recognize patterns, make predictions, and solve various tasks in machine learning and artificial intelligence. It can range from a single layer to multiple layers of interconnected nodes (neurons) organized into layers . This is AI neural network . Like human brains, artificial neural networks are made up of neurons that are connected like brain cells. These neurons process and receive information from nearby neurons before sending it to other neurons.

11. Artificial Neural Networks Architecture There are three layers in the network architecture: ANNs are structured in layers, with each layer consisting of multiple neurons. The common types of layers are : Input Layer: The first layer, which receives the input features . Hidden Layers : Intermediate layers between the input and output layers, where the network processes and transforms the inputs. The term "deep learning" refers to ANNs with many hidden layers . Output Layer: The final layer that produces the network's output, such as a class label or a numerical prediction. A typical feedforward network processes information in one direction, from input to output. Because of the numerous layers are sometimes referred to as the MLP (Multi-Layer Perceptron ). Backpropagation : A method used to calculate the gradient of the loss function with respect to each weight by propagating the error backward through the network.

12. How do Artificial Neural Networks Learn? Starting Point : Imagine you’re building a robot brain, but initially, it knows nothing. So , you randomly assign some strengths to the connections between its “neurons” (like how our brain’s neurons are connected). Seeing Data: Now, show the robot some examples of what you want it to learn. For instance, if you’re teaching it to recognize cats, show it lots of pictures of cats. Guessing and Checking : The robot tries to imagine what it’s seeing based on the strengths of its connections. At first, it’ll make lots of mistakes because it’s just guessing randomly. Getting Feedback: You tell the robot how wrong its guesses are. For example, you say, “No, that’s not a cat; it’s a dog.” This helps the robot understand where it went wrong and adjust through feedback loops. Adjusting Strengths: The robot tweaks the strengths of its connections based on the feedback. If it guessed wrong, it changes the connections to be a bit stronger or weaker so that next time it might make a better guess. This learning process helps the robot improve its accuracy over time. Practice Makes Perfect: The robot keeps looking at more examples, guessing, getting feedback, and adjusting until it gets better and better at recognizing cats. Testing Skills: Once the robot has seen lots of examples and adjusted its connections a lot, you give it a new picture it hasn’t seen before to see if it can correctly identify whether it’s a cat or not.

13. Application of Artificial Neural Networks 1 . Image Processing and Character recognition ANN algorithms play a significant part in picture and character recognition because of their capacity to take in many inputs, process them, and infer hidden and complicated, non-linear correlations. Character recognition, such as handwriting recognition, has many applications in fraud detection (for example, bank fraud) and even national security assessments. Image recognition is a rapidly evolving discipline with several applications ranging from social media facial recognition to cancer detection in medicine to satellite image processing for agricultural and defense purposes. Deep neural networks, which form the core of “deep learning,” have now opened up all of the new and transformative advances in computer science, speech recognition, and natural language processing – notable examples being self-driving vehicles, and other applications powered by neural nets.

14. Cont… 2. Forecasting Everyday company decisions (sales, the financial allocation between goods, and capacity utilization), economic and monetary policy, finance, and the stock market widely use it. Forecasting issues are frequently complex; for example, predicting stock prices is complicated with many underlying variables (some known, some unseen). Traditional forecasting models have flaws when it comes to accounting for these complicated, non-linear interactions. Given its capacity to model and extract previously unknown characteristics and correlations, ANNs can provide a reliable alternative when used correctly even in unsupervised learning scenarios. ANN also has no restrictions on the input and residual distributions, unlike conventional models . So , this AI neural network applications.

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