newgrnn

(To be removed) Design generalized regression neural network

newgrnn 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 = newgrnn(P,T,spread)


Description

Generalized regression neural networks (grnns) are a kind of radial basis network that is often used for function approximation. grnns can be designed very quickly.

net = newgrnn(P,T,spread) takes three inputs,

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

and returns a new generalized regression neural network.

The larger the spread, the smoother the function approximation. To fit data very closely, use a spread smaller than the typical distance between input vectors. To fit the data more smoothly, use a larger spread.

Properties

newgrnn creates a two-layer network. The first layer has radbas neurons, and calculates weighted inputs with dist and net input with netprod. The second layer has purelin neurons, calculates weighted input with normprod, and net inputs with netsum. Only the first layer has biases.

newgrnn 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 W2 are set to T.

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 = newgrnn(P,T);

The network is simulated for a new input.

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

References

Wasserman, P.D., Advanced Methods in Neural Computing, New York, Van Nostrand Reinhold, 1993, pp. 155–61

Version History

Introduced before R2006a

R2026a: newgrnn will be removed

newgrnn 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.