Neural state-space model with identifiable network weights

*Since R2022b*

expand all in page

## Description

Use `idNeuralStateSpace`

to create a black-box continuous-time or discrete-time neural state-space model with identifiable (estimable) network weights and bias. You can use the trained black-box model for control, estimation, optimization, and reduced order modeling.

Continuous-time neural state-space models have the following general form,

$$\begin{array}{l}\dot{x}\left(t\right)=F\left(t,x\left(t\right),u\left(t\right)\right)\\ y\left(t\right)=\left[\begin{array}{c}{y}_{1}(t)\\ {y}_{2}(t)\end{array}\right]=\left[\begin{array}{c}x\left(t\right)+{e}_{1}(t)\\ H\left(t,x\left(t\right),u\left(t\right)\right)+{e}_{2}(t)\end{array}\right]\end{array}$$

where the state function *F* and the nontrivial output function *H* are approximated by neural networks. Because you need to measure all the states to properly train the state function, the states measurements are considered to be part of the output function. Here, *e _{1}* and

*e*are measurement noises in the data sets which are minimized by the network training algorithm.

_{2}For discrete-time state-space systems, the state and output functions have this form.

$$\begin{array}{l}x\left(t+1\right)=F\left(t,x\left(t\right),u\left(t\right)\right)\\ y\left(t\right)=\left[\begin{array}{c}{y}_{1}(t)\\ {y}_{2}(t)\end{array}\right]=\left[\begin{array}{c}x\left(t\right)+{e}_{1}(t)\\ H\left(t,x\left(t\right),u\left(t\right)\right)+{e}_{2}(t)\end{array}\right]\end{array}$$

For more information on neural state-space models, see What are Neural State-Space Models?.

## Creation

### Syntax

`nss = idNeuralStateSpace(nx)`

`nss = idNeuralStateSpace(___,Name=Value)`

### Description

creates an autonomous (no-input) time-invariant continuous-time neural state-space object with `nss`

= idNeuralStateSpace(nx)`nx`

state variables and output identical to state.

example

specifies name-value pair arguments after any of the input argument in the previous syntax. You can use name-value pair arguments to set the number of inputs and outputs and other system configurations such as time domain, whether the system is time invariant and whether the system output has feed-through.`nss`

= idNeuralStateSpace(___,Name=Value)

For example, `nss = idNeuralStateSpace(3,NumInputs=2,NumOutputs=4,Ts=0.1)`

creates a time-invariant discrete-time neural state-space object with `3`

states, `2`

inputs, four outputs (the first three are state measurements), and sample time `0.1`

. The system is also time invariant (both state and output functions do not explicitly depend on time) and does not have direct feed-through (the input does not have immediate impact on output).

example

### Input Arguments

expand all

`nx`

— Number of state variables

positive integer

Number of state variables, specified as a positive integer.

**Example: **2

**Name-Value Arguments**

Specify optional pairs of arguments as `Name1=Value1,...,NameN=ValueN`

, where `Name`

is the argument name and `Value`

is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Use name-value pair arguments to specify `NumInputs`

, `NumOutputs`

and the `Ts`

, `IsTimeInvariant`

, and `HasFeedthrough`

properties of `nss`

.

**Example: **`Ts=0.1`

`NumInputs`

— Number of input variables

0 (default) | nonnegative integer

Number of input variables, specified as a nonnegative integer.

**Example: **`NumInputs=2`

`NumOutputs`

— Number of output variables

`nx`

(default) | nonnegative integer

Number of output variables, specified as a positive integer greater than or equal to `nx`

. The value must be greater than `nx`

because all the states are measured.

For example, if `nx`

is `2`

, `NumOutputs=4`

means that the state space system has four outputs, with the first two outputs being state measurements, and the last two are outputs from the output function *H*.

**Example: **`NumOutputs=4`

`HasFeedthrough`

— Option to set direct feedthrough

`false`

(default) | `true`

Option to set direct feedthrough, specified as one of the following:

`true`

— the nontrivial output measurement*y*is an explicit function of the input, that is_{2}*y*(_{2}*t*) =*H*(*t*,*x*,*u*).`false`

— the nontrivial output measurement*y*is not an explicit function of the input, even if_{2}`NumInputs`

is greater than zero. This is the default case, and*y*(_{2}*t*) =*H*(*t*,*x*).

This argument sets the value of the read-only property `FeedthroughInOutputNetwork`

of `nss`

.

**Example: **`false`

## Properties

expand all

`StateNetwork`

— State function network

`dlnetwork`

object

State function network, specified as a dlnetwork (Deep Learning Toolbox) object. This network approximates the state function of the state-space system (*F*). For continuous state-space systems the state function returns the system state derivative with respect to time, while for discrete-time state-space systems it returns the next state. The inputs of the function are time (if `IsTimeInvariant`

is `false`

), the current state, and the current input (if `NumInputs`

is positive), in that order.

When an `idNeuralStateSpace`

model is constructed, a default state network is created. It is a multi-layer perceptron (MLP) network with the following features:

Two hidden layers: each is a fully-connected layer with 64 nodes.

Two activation layers: each featuring a hyperbolic tangent (tanh) function.

One output layer: a fully-connected layer with

`nx`

nodes.

To change the default network configuration, use createMLPNetwork. For example:

nss.StateNetwork = createMLPNetwork(nss,"state",... LayerSizes=[64 64 64],... Activations="sigmoid")

You can also directly assign a custom `dlnetwork`

object as the state function network when the sizes of the input layers are distinguishable. For example:

nss.StateNetwork = dlnet;

where `dlnet`

is a custom `dlnetwork`

object with the configuration as mentioned under the `dlnet`

argument in setNetwork.

For more information on custom networks and how to assign them to an `idNeuralStateSpace`

object when the input layer sizes are not distinguishable, see setNetwork.

To train both state and output networks, use nlssest. For example:

` options1 = nssTrainingOptions("adam"); nss = nlssest(U, Y, nss, options1);`

**Note**

To train the network, use

`nlssest`

which updates the weights and biases of the network. After training completes, the network weights and biases are said to be "trained".A new training starts with the previously trained network. To reset the network, you can either manually assign the learnables of the

`dlnetwork`

object or initialize it.Multi-layer perceptron (MLP) networks with at least one hidden layer featuring squashing functions (such as hyperbolic tangent or sigmoid) are universal approximators, that is, are theoretically capable of approximating any function to any desired degree of accuracy provided that sufficiently many hidden units are available.

Deeper networks (networks with more hidden layers) can approximate compositional functions as well as shallow networks but with exponentially lower number of training parameters and sample complexity.

`OutputNetwork`

— Output function networks

`dlnetwork`

object

Output function networks, specified as a 2-by-1 array of dlnetwork (Deep Learning Toolbox) objects. The first network represents the identity relation between *y _{1}* and

*x*, since all the states are measured. This network has no learnable parameters, is fixed, and cannot be changed or trained.

The second network approximates the output function *H* of the state-space system, which is a function of time (if `IsTimeInvariant`

is `false`

), the current state, and the current input (if `NumInputs`

is positive and `HasFeedthrough`

is `true`

), in that order.

When you create an `idNeuralStateSpace`

model, the default network created to approximate *H* is a multi-layer perceptron (MLP) network with the following features:

Two hidden layers: each is a fully-connected layer with 64 nodes.

See AlsoQuantify Image Quality Using Neural Image Assessment - MATLAB & Simulink - MathWorks 日本setNetwork - Assign dlnetwork object as the state or output function of a neural state-space model - MATLAB - MathWorks 日本dlnetwork - 深層学習ニューラル ネットワーク - MATLAB - MathWorks 日本Generate Untargeted and Targeted Adversarial Examples for Image Classification - MATLAB & Simulink - MathWorks 日本Two activation layers: each featuring a hyperbolic tangent (tanh) function.

One output layer: a fully-connected layer with

`NumOutputs`

-`nx`

nodes.

To change the default network configuration, use createMLPNetwork. For example:

nss.OutputNetwork = createMLPNetwork(nss,"output",... LayerSizes=[64 64 64],... Activations="sigmoid")

You can also directly assign a custom `dlnetwork`

object as the output function network when the sizes of the input layers are distinguishable. For example:

nss.OutputNetwork = dlnet;

where `dlnet`

is a custom `dlnetwork`

object with the configuration as mentioned under the `dlnet`

argument in setNetwork.

For more information on custom networks and how to assign them to an `idNeuralStateSpace`

object when the input layer sizes are not distinguishable, see setNetwork.

To train both state and output networks, use nlssest. For example:

options1 = nssTrainingOptions("adam")options2 = nssTrainingOptions("sgdm")nss = nlssest(U, Y, nss, [options1; options2])

`Encoder`

— Encoder function network

`[]`

(default) | `dlnetwork`

object

Encoder function network, specified as a dlnetwork (Deep Learning Toolbox) object. The encoder maps the state to a latent state (usually, of a lower dimension), which is the input to the state function network. The dimension of this latent state is specified by LatentDim.

The default value is `[]`

, which means that you are not using the encoder. You add an encoder to your model by changing the default value of `LatentDim`

. This encoder network, by default contains two layers of size 64 with `tanh`

as the activation function.

To change the default encoder network configuration, use createMLPNetwork. For example:

nss.Encoder = createMLPNetwork(nss,"encoder",... LayerSizes=[4 4],... Activations="sigmoid")

You can also directly assign a custom `dlnetwork`

object as the encoder function network. For example:

nss.Encoder = dlnet;

where `dlnet`

is a custom `dlnetwork`

object with one input representing the original state and one output representing the latent state.

For more information on autoencoders, see What are Neural State-Space Models?

`Decoder`

— Decoder function network

`[]`

(default) | `dlnetwork`

object

Decoder function network, specified as a dlnetwork (Deep Learning Toolbox) object. The output of the state function network is the input of the decoder. The decoder maps the latent state back to the original state. The dimension of the latent state is specified by LatentDim.

The default value is `[]`

, which means that you are not using the decoder. You add a decoder to your model by changing the default value of `LatentDim`

. This decoder network, by default, contains two layers of size 64 with `tanh`

as the activation function.

To change the default decoder network configuration, use createMLPNetwork. For example:

nss.Decoder = createMLPNetwork(nss,"decoder",... LayerSizes=[4 4],... Activations="sigmoid")

You can also directly assign a custom `dlnetwork`

object as the decoder function network. For example:

nss.Decoder = dlnet;

where `dlnet`

is a custom `dlnetwork`

object with one input representing the latent state and one output representing the original state.

For more information on autoencoders, see What are Neural State-Space Models?

`LatentDim`

— Dimension of internal state

`NaN`

(default) | finite positive integer

Dimension of the internal (latent) state, specified as `NaN`

or a positive integer. To add an encoder or decoder to your model, specify `LatentDim`

as a positive integer.

`LatentDim` Value | Model Framework |
---|---|

`NaN` | |

A finite positive scalar | |

**Example: **`2`

`IsTimeInvariant`

— Flag indicating time invariance

`true`

(default) | `false`

Flag indicating time invariance, returned as one of the following:

`true`

— (default), the system is time invariant, neither the state function*F*of the output function*H*depend explicitly on time.`false`

— the system is time varying, both the state of the output function depend explicitly on time.

This property is read-only and cannot be set using dot notation. You can only specify this properly when you create `nss`

. To do so, use the corresponding name-value pair argument in `idNeuralStateSpace`

. For example:

nss = idNeuralStateSpace(3,NumInputs=2,IsTimeInvariant=false)

`FeedthroughInOutputNetwork`

— Flag indicating direct feedthrough

`false`

(default) | `true`

| array of `logical`

Flag indicating direct feedthrough in the output networks, returned as `false`

or as an array logical values.

If `NumOutputs`

= `nx`

, `FeedthroughInOutputNetwork`

is `false`

, because the only output is the measured state, and there is no contribution from any input.

If `NumOutputs`

> `nx`

, `FeedthroughInOutputNetwork`

is a 1-by-2 logical array in which the elements are as follows.

The first logical value corresponds to

*y*and is always false._{1}The second value corresponds to

*y*and is the same value that you specify with the name-value pair argument_{2}`HasFeedThrough`

when you create the object. When this value is true, then*y*is an explicit function of the input, otherwise, as default, there is no explicit contribution from the input to_{2}*y*._{2}

**Note**

This property is read-only and you can change it only when you create `nss`

, using the `HasFeedThrough`

argument in `idNeuralStateSpace`

.

**Example: **`[false, false]`

`StateName`

— State names

`{'x1','x2',...}`

(default) | character vector | cell array of character vectors

State names, specified as one of these values:

Character vector — For first-order models

Cell array of character vectors — For models with two or more states

`''`

— For unnamed states

You can specify `StateName`

using a string, such as `"velocity"`

, but the state name is stored as a character vector, `'velocity'`

.

**Example: ** `{'velocity','distance'}`

`StateUnit`

— State units

`{''}`

(default) | character vector | cell array of character vectors or stings | string | string array

State units, specified as:

A character vector or string — For first-order models

A cell array of character vectors or string array — For models with two or more states

`''`

— For states without specified units

Use `StateUnit`

to keep track of the units each state is expressed in. `StateUnit`

has no effect on system behavior.

If you specify `StateUnit`

using a string, such as `"mph"`

, the state units are stored as a character vector, `'mph'`

.

**Example: ** `'mph'`

**Example: **`{'rpm','rad/s'}`

`TimeVariable`

— Independent variable name

`"t"`

(default) | string | char vector

Independent variable name, specified as a string or character vector, for the state, input and output functions.

**Example: **`"t"`

`NoiseVariance`

— Innovation covariance matrix

matrix

Innovation covariance matrix, specified as an `NumOutputs`

-by-`NumOutputs`

positive semi-definite matrix. Typically this property is automatically set by the estimation algorithm.

**Example: **`1e-3*eye(2)`

`InputName`

— Names of input channels

`{''}`

(default) | character vector | cell array of character vectors | string | string array

Names of input channels, specified as:

A character vector or string — For single-input models

A cell array of character vectors or a string array — For models with two or more inputs

`''`

— For inputs without specified names

You can use automatic vector expansion to assign input names for multi-input models. For example, if `sys`

is a two-input model, you can specify `InputName`

as follows.

`sys.InputName = 'controls';`

The input names automatically expand to `{'controls(1)';'controls(2)'}`

.

You can use the shorthand notation `u`

to refer to the `InputName`

property. For example, `sys.u`

is equivalent to `sys.InputName`

.

Input channel names have several uses, including:

Identifying channels on model display and plots

Extracting subsystems of MIMO systems

Specifying connection points when interconnecting models

If you specify `InputName`

using a string or string array, such as `"voltage"`

, the input name is stored as a character vector, `'voltage'`

.

When you estimate a model using an `iddata`

object, `data`

, the software automatically sets `InputName`

to `data.InputName`

.

`InputUnit`

— Units of input signals

`{''}`

(default) | character vector | cell array of character vectors | string | string array

Units of input signals, specified as:

A character vector or string — For single-input models

A cell array of character vectors or string array — For models with two or more inputs

`''`

— For inputs without specified units

Use `InputUnit`

to keep track of the units each input signal is expressed in. `InputUnit`

has no effect on system behavior.

If you specify `InputUnit`

using a string, such as `"voltage"`

, the input units are stored as a character vector, `'voltage'`

.

**Example: ** `'voltage'`

**Example: **`{'voltage','rpm'}`

`InputGroup`

— Input channel groups

structure with no fields (default) | structure

Input channel groups, specified as a structure where the fields are the group names and the values are the indices of the input channels belonging to the corresponding group. When you use `InputGroup`

to assign the input channels of MIMO systems to groups, you can refer to each group by name when you need to access it. For example, suppose you have a five-input model `sys`

, where the first three inputs are control inputs and the remaining two inputs represent noise. Assign the control and noise inputs of `sys`

to separate groups.

sys.InputGroup.controls = [1:3];sys.InputGroup.noise = [4 5];

Use the group name to extract the subsystem from the control inputs to all outputs.

`sys(:,'controls')`

**Example: ** `struct('controls',[1:3],'noise',[4 5])`

`OutputName`

— Names of output channels

`{''}`

(default) | character vector | cell array of character vectors or strings | string | string array

Names of output channels, specified as:

A character vector or string— For single-output models

A cell array of character vectors or string array — For models with two or more outputs

`''`

— For outputs without specified names

You can use automatic vector expansion to assign output names for multi-output models. For example, if `sys`

is a two-output model, you can specify `OutputName`

as follows.

`sys.OutputName = 'measurements';`

The output names automatically expand to `{'measurements(1)';'measurements(2)'}`

.

You can use the shorthand notation `y`

to refer to the `OutputName`

property. For example, `sys.y`

is equivalent to `sys.OutputName`

.

Output channel names have several uses, including:

Identifying channels on model display and plots

Extracting subsystems of MIMO systems

Specifying connection points when interconnecting models

If you specify `OutputName`

using a string, such as `"rpm"`

, the output name is stored as a character vector, `'rpm'`

.

When you estimate a model using an `iddata`

object, `data`

, the software automatically sets `OutputName`

to `data.OutputName`

.

`OutputUnit`

— Units of output signals

`{''}`

(default) | character vector | cell array of character vectors | string | string array

Units of output signals, specified as:

A character vector or string — For single-output models

A cell array of character vectors or string array — For models with two or more outputs

`''`

— For outputs without specified units

Use `OutputUnit`

to keep track of the units each output signal is expressed in. `OutputUnit`

has no effect on system behavior.

If you specify `OutputUnit`

using a string, such as `"voltage"`

, the output units are stored as a character vector, `'voltage'`

.

**Example: ** `'voltage'`

**Example: **`{'voltage','rpm'}`

`OutputGroup`

— Output channel groups

structure with no fields (default) | structure

Output channel groups, specified as a structure where the fields are the group names and the values are the indices of the output channels belonging to the corresponding group. When you use `OutputGroup`

to assign the output channels of MIMO systems to groups, you can refer to each group by name when you need to access it. For example, suppose you have a four-output model `sys`

, where the second output is a temperature, and the rest are state measurements. Assign these outputs to separate groups.

sys.OutputGroup.temperature = [2];sys.OutputGroup.measurements = [1 3 4];

Use the group name to extract the subsystem from all inputs to the measurement outputs.

`sys('measurements',:)`

**Example: ** `struct('temperature',[2],'measurement',[1 3 4])`

`Notes`

— Text notes about model

`[0×1 string]`

(default) | string | character vector | cell array of character vectors or strings | string array

Text notes about the model, specified as a string or character vector. The property stores whichever of these two data types you provide. For instance, suppose that `sys1`

and `sys2`

are dynamic system models. You can set their `Notes`

properties to a string and a character vector, respectively.

sys1.Notes = "sys1 has a string.";sys2.Notes = 'sys2 has a character vector.';sys1.Notessys2.Notes

ans = "sys1 has a string."ans = 'sys2 has a character vector.'

You can also specify `Notes`

as string array or a cell array of character vectors or strings.

`UserData`

— Data associated with model

`[]`

(default) | any data type

Data of any kind that you want to associate and store with the model, specified as any MATLAB^{®} data type.

`Ts`

— Sample time

nonnegative scalar

Sample time, specified as a nonnegative scalar, in units specified by the `TimeUnit`

property. For a continuous time model, `Ts`

is equal to 0 (default). Changing the value of Ts has no impact on the system data and does not discretize or resample the model.

**Note**

If you change `Ts`

to a different value after networks are trained, you need to train the networks again because the original trained networks are no longer valid.

**Example: **`0.1`

`TimeUnit`

— Model time units

`'seconds'`

(default) | `'minutes'`

| `'milliseconds'`

| ...

Model time units, specified as:

`'nanoseconds'`

`'microseconds'`

`'milliseconds'`

`'seconds'`

`'minutes'`

`'hours'`

`'days'`

`'weeks'`

`'months'`

`'years'`

If you specify `TimeUnit`

using a string, such as `"hours"`

, the time units are stored as a character vector, `'hours'`

.

Model properties such as sample time `Ts`

, `InputDelay`

, `OutputDelay`

, and other time delays are expressed in the units specified by `TimeUnit`

. Changing this property has no effect on other properties, and therefore changes the overall system behavior. Use chgTimeUnit to convert between time units without modifying system behavior.

`Report`

— Summary report

report field values

This property is read-only.

Summary report that contains information about the estimation options and results for a state-space model obtained using estimation commands. Use `Report`

to find estimation information for the identified model, including the:

Status (estimated or constructed)

Estimation method

Estimation options

Search termination conditions

Estimation data fit and other quality metrics

For more information on this property and how to use it, see the Output Arguments section of the corresponding estimation command reference page and Estimation Report.

## Object Functions

createMLPNetwork | Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system |

setNetwork | Assign `dlnetwork` object as the state or output function of a neural state-space model |

generateMATLABFunction | Generate MATLAB functions that evaluate the state and output functions, and their Jacobians, of a nonlinear grey-box or neural state-space model |

sim | Simulate response of identified model |

idNeuralStateSpace/evaluate | Evaluate a neural state-space system for a given set of state and input values and return state derivative (or next state) and output values |

idNeuralStateSpace/linearize | Linearize a neural state-space model around an operating point |

## Examples

collapse all

### Create Continuous-Time Neural State-Space Object

Open Live Script

Use idNeuralStateSpace to create a continuous-time neural state-space object with two states, no inputs, and outputs identical to states.

nss = idNeuralStateSpace(2)

nss =Continuous-time Neural ODE in 2 variables dx/dt = f(x(t)) y(t) = x(t) + e(t) f(.) network: Deep network with 2 fully connected, hidden layers Activation function: tanh Variables: x1, x2 Status: Created by direct construction or transformation. Not estimated.

Use dot notation to access the object properties.

nss.StateNetwork

ans = dlnetwork with properties: Layers: [6x1 nnet.cnn.layer.Layer] Connections: [5x2 table] Learnables: [6x3 table] State: [0x3 table] InputNames: {'x'} OutputNames: {'dxdt'} Initialized: 1 View summary with summary.

nss.Name = "myNssObject";nss.UserData = ['Created on ' char(datetime)]

nss =Continuous-time Neural ODE in 2 variables dx/dt = f(x(t)) y(t) = x(t) + e(t) f(.) network: Deep network with 2 fully connected, hidden layers Activation function: tanh Variables: x1, x2 Status: Created by direct construction or transformation. Not estimated.

You can now re-configure the state network using createMLPNetwork, if needed, and then use time-domain data to perform estimation and validation.

### Create Discrete-Time Neural State-Space Object

Open Live Script

Use idNeuralStateSpace to create a discrete-time neural state-space object with three states, two inputs, four outputs, and sample time `0.1`

.

nss = idNeuralStateSpace(3,NumInputs=2,NumOutputs=4,Ts=0.1)

nss =Discrete-time Neural State-Space Model with 4 outputs, 3 states, and 2 inputs x(t+1) = f(x(t),u(t)) y_1(t) = x(t) + e_1(t) y_2(t) = g(x(t),u(t)) + e_2(t) y(t) = [y_1(t); y_2(t)] f(.) network: Deep network with 2 fully connected, hidden layers Activation function: tanhg(.) network: Deep network with 2 fully connected, hidden layers Activation function: tanh Inputs: u1, u2Outputs: y1, y2, y3, y4States: x1, x2, x3Sample time: 0.1 seconds Status: Created by direct construction or transformation. Not estimated.

Use dot notation to access the object properties.

nss.OutputNetwork.Layers

ans = 5x1 Layer array with layers: 1 'x[k]' Feature Input 3 features 2 'u[k]' Feature Input 2 features 3 'yx' Function @(x)x(:) 4 'yu' Function @(u)zeros(nx,nu)*u(:) 5 'y[k]' Addition Element-wise addition of 2 inputs

ans = 9x1 Layer array with layers: 1 'x[k]' Feature Input 3 features 2 'fc1' Fully Connected 64 fully connected layer 3 'act1' Tanh Hyperbolic tangent 4 'fc2' Fully Connected 64 fully connected layer 5 'act2' Tanh Hyperbolic tangent 6 'yx' Fully Connected 1 fully connected layer 7 'u[k]' Feature Input 2 features 8 'yu' Function @(u)zeros(ny,nu)*u 9 'y[k]' Addition Element-wise addition of 2 inputs

`nss.UserData = ['Created on ' char(datetime)];nss.UserData`

ans = 'Created on 20-Jul-2024 13:09:43'

Note that by default the output does not explicitly depend on the input.

nss.FeedthroughInOutputNetwork

`ans = `*1x2 logical array* 0 0

You can now re-configure the state and output networks using createMLPNetwork, if needed, and then use time-domain data to perform estimation and validation.

## References

[1] Chen, Ricky T. Q., Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. “Neural Ordinary Differential Equations.” arXiv, December 13, 2019. http://arxiv.org/abs/1806.07366.

## Version History

**Introduced in R2022b**

## See Also

### Objects

- nssTrainingADAM | nssTrainingSGDM | nssTrainingRMSProp | nssTrainingLBFGS | idss | idnlgrey

### Functions

- createMLPNetwork | setNetwork | nssTrainingOptions | nlssest | generateMATLABFunction | idNeuralStateSpace/evaluate | idNeuralStateSpace/linearize | sim

### Blocks

- Neural State-Space Model

### Live Editor Tasks

- Estimate Neural State-Space Model

### Topics

- What are Neural State-Space Models?
- Estimate Neural State-Space System
- Estimate Nonlinear Autonomous Neural State-Space System
- Neural State-Space Model of Simple Pendulum System
- Reduced Order Modeling of a Nonlinear Dynamical System using Neural State-Space Model with Autoencoder
- Augment Known Linear Model with Flexible Nonlinear Functions

## MATLAB コマンド

次の MATLAB コマンドに対応するリンクがクリックされました。

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