newlind

(To be removed) Design linear layer

newlind 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 = newlind(P,T,Pi)


Description

net = newlind(P,T,Pi) takes these input arguments,

P	R-by-Q matrix of Q input vectors
T	S-by-Q matrix of Q target class vectors
Pi	1-by-ID cell array of initial input delay states

where each element Pi{i,k} is an Ri-by-Q matrix, and the default = []; and returns a linear layer designed to output T (with minimum sum square error) given input P.

newlind(P,T,Pi) can also solve for linear networks with input delays and multiple inputs and layers by supplying input and target data in cell array form:

P	Ni-by-TS cell array	Each element P{i,ts} is an Ri-by-Q input matrix
T	Nt-by-TS cell array	Each element P{i,ts} is a Vi-by-Q matrix
Pi	Ni-by-ID cell array	Each element Pi{i,k} is an Ri-by-Q matrix, default = []

and returns a linear network with ID input delays, Ni network inputs, and Nl layers, designed to output T (with minimum sum square error) given input P.

Examples

You want a linear layer that outputs T given P for the following definitions:

P = [1 2 3];
T = [2.0 4.1 5.9];

Use newlind to design such a network and check its response.

net = newlind(P,T);
Y = sim(net,P)

You want another linear layer that outputs the sequence T given the sequence P and two initial input delay states Pi.

P = {1 2 1 3 3 2};
Pi = {1 3};
T = {5.0 6.1 4.0 6.0 6.9 8.0};
net = newlind(P,T,Pi);
Y = sim(net,P,Pi)

You want a linear network with two outputs Y1 and Y2 that generate sequences T1 and T2, given the sequences P1 and P2, with three initial input delay states Pi1 for input 1 and three initial delays states Pi2 for input 2.

P1 = {1 2 1 3 3 2}; Pi1 = {1 3 0};
P2 = {1 2 1 1 2 1}; Pi2 = {2 1 2};
T1 = {5.0 6.1 4.0 6.0 6.9 8.0};
T2 = {11.0 12.1 10.1 10.9 13.0 13.0};
net = newlind([P1; P2],[T1; T2],[Pi1; Pi2]);
Y = sim(net,[P1; P2],[Pi1; Pi2]);
Y1 = Y(1,:)
Y2 = Y(2,:)

Algorithms

newlind calculates weight W and bias B values for a linear layer from inputs P and targets T by solving this linear equation in the least squares sense:

[W b] * [P; ones] = T

Version History

Introduced before R2006a

R2026a: newlind will be removed

newlind 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.