Artificial Intelligence-Computational Intelligence in Python

Examine fuzzy logic systems that map crisp inputs to fuzzy sets with membership degrees using a rule base and inference engine. Learn how defuzzification converts aggregated outputs into crisp results.

Explore a fuzzy inference system that models tipping based on service and food quality, using triangular membership functions, centroid method to obtain a crisp tip value, and Python implementation.

Explore solving a tip calculation problem with a fuzzy inference system by defining input ranges for service and quality, setting triangular membership functions, and outlining the setup before computation.

Explore fuzzy rules that map input variables food and service to output levels low, medium, and high through fuzzification, membership functions, and rule implication.

Aggregate the output membership functions with the max operator across low, medium, and high activations, then perform defuzzification using the centroid method to obtain the crisp output.

Explore how a fuzzy inference system uses the centroid method to produce interpretable outputs, such as a 20.99% tip, and learn about optimization with genetic algorithms.

Explore how evolutionary algorithms solve optimization problems by evolving a population of candidate solutions, using fitness, selection, crossover, and mutation to improve over generations.

Explore how a genetic algorithm initializes a population, encodes genes into chromosomes, and uses fitness evaluation, selection, crossover, mutation, and elitism to evolve optimal solutions.

Implement a genetic algorithm to optimize a six-input function using a fitness function that compares the produced output to the desired output, employing the Bigart library for population evaluation.

Initialize a population for a genetic algorithm by setting the number of chromosomes and six genes per chromosome, choosing a -2 to 5 range, and filling with random uniform values.

Set genetic algorithm parameters: generations, population size, and eight parents. Use roulette wheel selection, single point crossover, and 10 percent mutation to drive the next generation.

Define a genetic algorithm constructor with generations, population, fitness function, genes, and range, using roulette selection, single-point crossover, and mutation capped at 10%; run, identify top solution, and predict output.

A genetic algorithm optimizes a solution vector to predict outcomes, with best values around 0.3309 and 0.776, a plateau near 60-65 generations, and an output near forty four.

Explore how the human neuron inspires artificial neurons, including weighted sums and activation. Learn to build and train a feedforward neural network with backpropagation for supervised learning in Python.

Develop a neural network to recognize digit labels from binary input patterns, using 20 inputs and 20 hidden nodes, scaling outputs by dividing by 10 to map 0–9.

Define the neural network class in Python with input, hidden (size 20), and output layers; initialize weight matrices with a Gaussian mean zero and apply sigmoid activation and its derivative.

Understand the forward pass and backward pass in a neural network, leveraging dot products, weights, and sigmoid activation, then train using back propagation to update weights.

Define a make predictions procedure to reshape input to five by four, perform a forward pass, and train the network for 5,000 epochs, reporting mean squared error.

Observe how the neural network's loss decreases through epochs using backpropagation, forward and backward passes, and training patterns for prediction, paving the way for hybrid intelligent systems.

Explore how to combine neural networks, fuzzy logic, and evolutionary computation into hybrid intelligent systems. Analyze strengths and weaknesses to achieve learning, transparency, and adaptability with imprecise knowledge.

Explore how adaptive neural networks overlay a fuzzy inference system to tune membership functions and rule strengths via a hybrid forward backward training approach.

Explore the Mackey-Glass chaotic time series governed by a differential equation, and learn how the previous four time stamps predict future values, specifically X plus six.

Download the dataset directory, install the required package, import the membership function libraries, read the time-series training data, and construct input and output variables for subsequent membership function design.

Analyze data statistics to design two-input membership functions, describe data ranges and means, and place four membership functions for each input using the observed distribution.

This lecture examines how a two-input system computes errors with brackets and membership functions, compares real versus predicted outputs, and notes performance limits with sharp kinks.

Four-input configurations with added membership functions for each input improve performance, adjust the filename and variables, and reduce the error to 0.0324.