||Create custom neural network|
Create and learn the basic components of a neural network object.
Learn how to manually configure the network before
training using the
Learn how the format of input data structures affects the simulation of networks.
Customize network architecture using its properties and use and train the custom network.
Design an adaptive linear system that responds to changes in its environment as it is operating.
Learn the architecture, design, and training of perceptron networks for simple classification problems.
Learn to design and use radial basis networks.
Use probabilistic neural networks for classification problems.
Learn to design a generalized regression neural network (GRNN) for function approximation.
Create and train a Learning Vector Quantization (LVQ) Neural Network.
Design a linear network that, when presented with a set of given input vectors, produces outputs of corresponding target vectors.
Design a network that stores a specific set of equilibrium points such that, when an initial condition is provided, the network eventually comes to rest at such a design point.
Learn the primary steps in a neural network design process.
Learn about a single-input neuron, the fundamental building block for neural networks.
Learn architecture of single- and multi-layer networks.
Use template functions to create custom functions that control algorithms to initialize, simulate, and train your networks.