Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system
Since R2022b
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Syntax
dlnet = createMLPNetwork(nss,type)
dlnet = createMLPNetwork(___,Name=Value)
Description
dlnet = createMLPNetwork(nss,type) creates a multi-layer perceptron (MLP) network dlnet of type type to approximate either the state, (the non-trivial part of) the output, the encoder, or the decoder function of the neural state space object nss. For example, to specify the network for the state function, use
nss.StateNetwork = createMLPNetwork(nss,"state",...)
To specify the network for the non-trivial part of the output function, use
nss.OutputNetwork(2) = createMLPNetwork(nss,"output",...)
To specify the encoder network configuration, use
nss.Encoder = createMLPNetwork(nss,"encoder",...)
To specify the decoder network configuration, use
nss.Decoder = createMLPNetwork(nss,"decoder",...)
example
dlnet = createMLPNetwork(___,Name=Value) 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 layers, the number of neurons per layer, or the type of their activation function.
For example, dlnet = createMLPNetwork(nss,"output",LayerSizes=[4 3],Activations="sigmoid") creates an output network with two hidden layers having four and three sigmoid-activated neurons, respectively.
Examples
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Create MLP Network for Neural State-Space Object
Open Live Script
Use idNeuralStateSpace to create a continuous-time neural state-space object with three states and one input. By default, the state network has two hidden layers each with 64 neurons and a hyperbolic tangent activation function.
nss = idNeuralStateSpace(3,NumInputs=1)
nss =Continuous-time Neural ODE in 3 variables dx/dt = f(x(t),u(t)) y(t) = x(t) + e(t) f(.) network: Deep network with 2 fully connected, hidden layers Activation function: tanh Variables: x1, x2, x3 Status: Created by direct construction or transformation. Not estimated.
Use createMLPNetwork and dot notation, to re-configure the state network. Specify three hidden layers of 4, 8 and 4 neurons, respectively, and use sigmoid as the activation function.
nss.StateNetwork = createMLPNetwork(nss,"state", ... LayerSizes=[4 8 4],Activations="sigmoid");
You can now use time-domain data to perform estimation and validation.
Input Arguments
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nss — Neural state-space object
idNeuralStateSpace object
Neural state-space object, specified as an idNeuralStateSpace object.
Example: idNeuralStateSpace(2,NumInputs=1)
type — Network type
"state" | "output" | "encoder" | "decoder"
Network type, specified as one of the following:
"state"— creates a network to approximate the state function of nss. 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 state function are time (ifIsTimeInvariantisfalse), the current state, and the current input (ifNumInputsis positive)."output"— creates a network to approximate the non-trivial part of the output function ofnss. This network returns the non-trivial system output, y2(t) = H(t,x,u), as a function of time (ifIsTimeInvariantisfalse), the current state, and the current input (ifNumInputsis positive). For more information, see idNeuralStateSpace."encoder"— creates a network to approximate the encoder function. The encoder maps the state to a latent state (usually, of a lower dimension), which is the input to the state function network. For more information, see idNeuralStateSpace."decoder"— creates a network to approximate the decoder function. The output of the state function network is the input of the decoder. The decoder maps the latent state back to the original state. For more information, see idNeuralStateSpace.
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.
Example: LayerSizes=[16 32 16]
Use name-value pair arguments to specify network properties such as the number of hidden layers, the size of each hidden layer, the activation functions, and the weights and bias initialization methods.
LayerSizes — Layer sizes
[64 64] (default) | vector of positive integers
Layer sizes, specified as a vector of positive integers. Each number specifies the number of neurons (network nodes) for each hidden layer (each layer is fully-connected). For example, [10 20 8] specifies a network with three hidden layers, the first (after the network input) having 10 neurons, the second having 20 neurons, and the last (before the network output), having 8 neurons. Note that the output layer is also fully-connected, and you cannot change its size.
Activations — Activation function type
"tanh" (default) | "sigmoid" | "relu" | "leakyRelu" | "clippedRelu" | "elu" | "gelu" | "swish" | "softplus" | "scaling" | "softmax" | "none"
Activation function type for all hidden layers, specified as one of the following: "tanh", "sigmoid", "relu", "leakyRelu", "clippedRelu", "elu", "gelu", "swish", "softplus", "scaling", or "softmax". All of these are available in Deep Learning Toolbox™. "softplus" and "scaling" also require Reinforcement Learning Toolbox™.
You can specify hyperparameter values for "leakyRelu", "clippedRelu", "elu", and "scaling". For example:
"leakyRelu(0.2)"specifies a leaky ReLU activation layer with a scaling value of0.2."clippedRelu(5)"specifies a clipped ReLU activation layer with a ceiling value of5."elu(2)"specifies an ELU activation layer with theAlphaproperty equal to2."scaling(0.2,4)"specifies a scaling activation layer with a scale of0.2and a bias of4.
Also, you can now choose to not use an activation function by specifying the activation function as "none".
For more information about these activations, see the Activation Layers and Utility Layers sections in List of Deep Learning Layers (Deep Learning Toolbox).
WeightsInitializer — Weights initializer method
"glorot" (default) | "he" | "orthogonal" | "narrow-normal" | "zeros" | "ones"
Weights initializer method for all hidden layers, specified as one of the following:
"glorot"— uses the Glorot method."he"— uses the He method."orthogonal"— uses the orthogonal method."narrow-normal"— uses the narrow-normal method."zeros"— initializes all weights to zero."ones"— initializes all weights to one.
BiasInitializer — Bias initializer method
"zeros" (default) | "ones" | "narrow-normal"
Bias initializer method for all hidden layers, specified as one of the following:
"narrow-normal"— uses the narrow-normal method."zeros"— initializes all biases to zero."ones"— initializes all biases to one.
Output Arguments
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dlnet — Network for the state or output function
dlnetwork object
Network for the state or output function of nss, specified as a dlnetwork (Deep Learning Toolbox) object.
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 state function are time (if IsTimeInvariant is false), the current state, and the current input (if NumInputs is positive).
The output function returns the system output as a function of time (if IsTimeInvariant is false), the current state, and the current input (if NumInputs is positive).
Note
You can use commands such as summary(dlnet), plot(dlnet), dlnet.Layers, and dlnet.Learnables to examine network details.
Version History
Introduced in R2022b
See Also
Objects
- idNeuralStateSpace | nssTrainingADAM | nssTrainingSGDM | nssTrainingRMSProp | nssTrainingLBFGS | idss | idnlgrey
Functions
- setNetwork | nssTrainingOptions | nlssest | generateMATLABFunction | idNeuralStateSpace/evaluate | idNeuralStateSpace/linearize | sim
Live Editor Tasks
- Estimate Neural State-Space Model
Blocks
- 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
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