Fundamentals of Artificial Neural Network with MATLAB

Explore how simple neurons represent classification and regression problems in MATLAB, using linear and nonlinear boundaries, linear equations, and polynomial or exponential forms.

This lecture shows how a single neuron solves linear classification by representing a line with weights and bias, using a threshold activation, and training to adjust weights and bias.

Analyze threshold logic unit, a neuron model with inputs, weights, bias, and a threshold activation function that outputs zero or one, relating it to biological neuron: dendrites, soma, axon, synapses.

Practice predicting a neuron's binary output using a simplified binary neuron model with inputs, weights, and a threshold, via the summation of inputs times weights and an activation function.

Explore how a trained neuron with two inputs defines a decision boundary by deriving the line from weights and threshold. Visualize and validate classifications with sample points on a graph.

Learn how a two-input neuron encodes a line with y = w1*x1 + w2*x2 = theta, giving the slope -w1/w2 and intercept theta/w2 for y = mx + c.

Learn to derive a neuron's weights and threshold from a given linear decision boundary. By matching slope and intercept, set w2=1, w1=2 and compute the threshold to classify data.

Explore artificial neural network fundamentals by examining a single neuron, its weights and threshold, and how training algorithms adjust connections for linear and logic gate applications.

implement a not gate using a single neuron with digital input and output, derive weight and threshold values, and understand how to represent logic gates with a neuron structure.

Show how a two-input, one-output neuron uses weights w1 and w2 and a threshold theta to implement the and gate, outputting 1 only for x1=x2=1.

learn how a single neuron can implement an or gate by selecting weights and a threshold; examples show w1=1.5, w2=1.5 with theta=0.5, with infinite solutions.

The XOR gate cannot be represented by a single neuron; requires interconnection of multiple neurons in a neural network.

Explore single-layer and multilayer feedforward networks for binary classification and decision boundaries, detailing the single neuron (bias, weights, summation, threshold) and how activation functions drive outputs.

Practice a two-layer feed-forward neural network with a minus-one bias and sign activation to determine input and output patterns using weights w1 and w2, classifying shaded area by boundary conditions.

Practice designing binary neural logic with threshold activation in feedforward networks, identifying logic functions from multi-input neurons, and implementing them with one-layer or two-layer met clock neurons.

Design a two-layer neural network with a single output to classify a shaded region by constructing decision boundaries and configuring weights and biases with a sine activation.

Review the simple neuron model, feedforward networks, and how binary activation functions reflect logic gates. We will study continuous activation, regression, and training algorithms in the next chapter.

Explore how neural networks learn by automatically adjusting weights to reduce error between output and target values, via input–output comparison and a learning rule.

Train a neural network with the perceptron learning rule by adjusting weights via the learning rate when the output differs from the expected, until all inputs match.

demonstrate the perceptron learning rule with a simple linear separable classification, showing how a single layer perceptron adjusts weights and bias via a learning rate to form the decision boundary.

Explore the perceptron learning rule for a single-layer binary perceptron, detailing weight updates, iterations, and epoch concepts, and compare manual calculation with MATLAB-based training and validation.

Identify the problem type and number of inputs to finalize the network design. Choose a feedforward network and a learning rule, and prepare training data in a MATLAB-ready matrix.

Practice perceptron training in MATLAB by updating weights via the perceptron learning rule for a bias-enabled input set, illustrating the first epoch.

Develop a neural network in MATLAB by creating a function, defining inputs and fixed weights, computing the net value, and producing an output of one or minus one for classification.

Develop a trained binary perceptron using MATLAB Simulink blocks, finalizing weights, applying input and bias, and observing outputs to classify inputs as plus one or minus one.

Learn to implement the perceptron learning rule in MATLAB, training with input vectors and a bipolar activation function to obtain updated weights after one epoch.

Develop and train a classifier neural network with MATLAB's Classification Learner toolbox, preparing data in Excel, training with linear discriminant, evaluating via confusion matrix, and exporting a MATLAB function.

Explore how regression problems extend neural networks from classification, using feedforward networks with linear and sigmoid activation to solve linear and nonlinear regression, including the delta learning rule.

Learn to train neural networks with continuous activation using the delta learning rule, derive weight updates via gradient of mean square error, and apply a learning rate to minimize error.

Derive the derivative term for different activation functions, including linear, unipolar sigmoid, and bipolar sigmoid, to implement the delta learning rule and write MATLAB code for weight updates.

Develop MATLAB code for a trained continuous perceptron, using linear, unipolar sigmoid, and bipolar sigmoid activations to compute net and activation with inputs as rows and weights as columns.

Learn to implement the delta learning rule in MATLAB by coding perceptron weight updates using unipolar and bipolar sigmoid activations, with training vectors, learning rate, and multi-epoch training.

Develop a simulation model of a continuous perceptron in MATLAB using Simulink, neural network toolbox transfer functions, and configurable activation, weights, and bias. Observe output with ramp input.

Design a continuous perceptron with linear activation to predict average weight from height using manual weight calculation for slope and bias. Use MATLAB code to train and test the model.

Explore training a continuous perceptron in MATLAB for linear regression, highlighting how weight initialization and learning rate shape weight updates and convergence across epochs.

Examine how two-layer feedforward networks extend single-layer models to address regression and complex mappings, with hidden layers, sigmoid activation, and Levenberg–Marquardt training in MATLAB.

Develop a two-layer feedforward neural network in MATLAB to model current-to-flux mapping for the carpeting problem, with bipolar sigmoid hidden and linear output, trained on 101 samples with 70/15/15 split.

Use the generated Simulink model in MATLAB simulations alongside other models, observe flux for different inputs, and view weight and bias values to adapt the model for power electronics applications.

Train a neural network with MATLAB's neural net fitting tool to map a sinusoidal function using 70/15/15 data split, 2–3 hidden neurons, and generate Simulink model with error below 0.02.

Design a two-layer feedforward neural network in matlab to predict old car prices from features like age, kilometers traveled, engine size, fuel type, horsepower, metallic color, and weight using regression.

Analyze data samples from an excel file by cleaning missing and incorrect values, identify outliers, and convert categorical inputs (fuel, color, transmission) into numerical inputs for neural network training.

Develop a MATLAB neural network to predict car price using age, mileage, and engine capacity with the neural network fitting app.

Develop and train a MATLAB neural network to predict car prices using age, mileage, and engine capacity; run simulations and display price forecasts.