newrbe

(To be removed) Design exact radial basis network

newrbe will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

net = newrbe(P,T,spread)


Description

Radial basis networks can be used to approximate functions. newrbe very quickly designs a radial basis network with zero error on the design vectors.

net = newrbe(P,T,spread) takes two or three arguments,

P	RxQ matrix of Q R-element input vectors
T	SxQ matrix of Q S-element target class vectors
spread	Spread of radial basis functions (default = 1.0)

and returns a new exact radial basis network.

The larger the spread is, the smoother the function approximation will be. Too large a spread can cause numerical problems.

Examples

Here you design a radial basis network given inputs P and targets T.

P = [1 2 3];
T = [2.0 4.1 5.9];
net = newrbe(P,T);

The network is simulated for a new input.

P = 1.5;
Y = sim(net,P)

Algorithms

newrbe creates a two-layer network. The first layer has radbas neurons, and calculates its weighted inputs with dist and its net input with netprod. The second layer has purelin neurons, and calculates its weighted input with dotprod and its net inputs with netsum. Both layers have biases.

newrbe sets the first-layer weights to P', and the first-layer biases are all set to 0.8326/spread, resulting in radial basis functions that cross 0.5 at weighted inputs of +/– spread.

The second-layer weights IW{2,1} and biases b{2} are found by simulating the first-layer outputs A{1} and then solving the following linear expression:

[W{2,1} b{2}] * [A{1}; ones] = T

Version History

Introduced before R2006a

R2026a: newrbe will be removed

newrbe will be removed in a future release.

To train a neural network with tabular data, use one of these functions:

• fitrnet (Statistics and Machine Learning Toolbox) — Train regression neural network. Use this function for simple regression tasks.

• fitcnet (Statistics and Machine Learning Toolbox) — Train classification neural network. Use this function for simple classification tasks.

• trainnet — Train neural network. Use this function when the fitrnet and fitcnet functions do not provide the training options that you need for your task.

To train a neural network with time series data, use one of these options:

• Time Series Modeler app — Interactively design and train neural networks for time series data.

• trainnet — Train neural network. Use this function when you need more flexibility in the network architecture or training algorithm.

To design and customize your own neural network for these workflows, you can create a network using an array of deep learning layers or a dlnetwork object. To create and edit neural networks interactively and generate code, use the Deep Network Designer app. To learn more about neural network building workflows, see these examples and topics:

• Build Networks with Deep Network Designer

• List of Deep Learning Layers

• Example Deep Learning Network Architectures

• Multiple-Input and Multiple-Output Networks

• Define Custom Deep Learning Layers

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.