Define Shallow Neural Network Architectures
Define shallow neural network architectures and algorithms
Functions
network | Create custom shallow neural network |
Examples and How To
Custom Neural Networks
- Create Neural Network Object
Create and learn the basic components of a neural network object. - Configure Shallow Neural Network Inputs and Outputs
Learn how to manually configure the network before training using theconfigurefunction. - Understanding Shallow Network Data Structures
Learn how the format of input data structures affects the simulation of networks. - Edit Shallow Neural Network Properties
Customize network architecture using its properties and use and train the custom network.
Historical and Alternative Neural Networks
- Adaptive Neural Network Filters
Design an adaptive linear system that responds to changes in its environment as it is operating. - Perceptron Neural Networks
Learn the architecture, design, and training of perceptron networks for simple classification problems. - Classification with a Two-Input Perceptron
A two-input hard limit neuron is trained to classify four input vectors into two categories. - Outlier Input Vectors
A 2-input hard limit neuron is trained to classify 5 input vectors into two categories. - Normalized Perceptron Rule
A 2-input hard limit neuron is trained to classify 5 input vectors into two categories. - Linearly Non-separable Vectors
A 2-input hard limit neuron fails to properly classify 5 input vectors because they are linearly non-separable. - Radial Basis Neural Networks
Learn to design and use radial basis networks. - Radial Basis Approximation
This example uses the NEWRB function to create a radial basis network that approximates a function defined by a set of data points. - Radial Basis Underlapping Neurons
A radial basis network is trained to respond to specific inputs with target outputs. - Radial Basis Overlapping Neurons
A radial basis network is trained to respond to specific inputs with target outputs. - GRNN Function Approximation
This example uses functions NEWGRNN and SIM. - PNN Classification
This example uses functions NEWPNN and SIM. - Probabilistic Neural Networks
Use probabilistic neural networks for classification problems. - Generalized Regression Neural Networks
Learn to design a generalized regression neural network (GRNN) for function approximation. - Learning Vector Quantization (LVQ) Neural Networks
Create and train a Learning Vector Quantization (LVQ) Neural Network. - Learning Vector Quantization
An LVQ network is trained to classify input vectors according to given targets. - Linear Neural Networks
Design a linear network that, when presented with a set of given input vectors, produces outputs of corresponding target vectors. - Linear Prediction Design
This example illustrates how to design a linear neuron to predict the next value in a time series given the last five values. - Adaptive Linear Prediction
This example shows how an adaptive linear layer can learn to predict the next value in a signal, given the current and last four values.
Concepts
- Workflow for Neural Network Design
Learn the primary steps in a neural network design process.
- Neuron Model
Learn about a single-input neuron, the fundamental building block for neural networks.
- Neural Network Architectures
Learn architecture of single- and multi-layer networks.
