| Neural Network Toolbox |
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Index
- ADALINE network
- decision boundary <1> <2>
- adaption
- custom function
- function
- parameters
- adaptive filter
- example
- noise cancellation example
- prediction application
- prediction example
- training
- adaptive linear networks <1> <2>
- amplitude detection
- applications
- adaptive filtering
- aerospace
- automotive
- banking
- defense
- electronics
- entertainment
- financial
- insurance
- manufacturing
- medical <1> <2>
- oil and gas exploration
- robotics
- speech
- telecommunications
- transportation
- architecture
- bias connection <1> <2>
- input connection <1> <2>
- input delays
- layer connection <1> <2>
- layer delays
- number of inputs <1> <2>
- number of layers <1> <2>
- number of outputs <1> <2>
- number of targets <1> <2>
- output connection <1> <2>
- target connection <1> <2>
- backpropagation <1> <2>
- algorithm
- example
- backtracking search
- batch training <1> <2> <3>
- dynamic networks
- static networks
- Bayesian framework
- benchmark <1> <2>
- BFGS quasi-Newton algorithm
- bias
- connection
- definition
- initialization function
- learning
- learning function
- learning parameters
- subobject <1> <2>
- value <1> <2>
- box distance
- Brent's search
- cell array
- derivatives
- errors
- initial layer delay states
- input P
- input vectors
- inputs and targets
- inputs property
- layer targets
- layers property
- matrix of concurrent vectors
- matrix of sequential vectors
- sequence of outputs
- sequential inputs
- tap delayed inputs
- weight matrices and bias vectors
- Charalambous' search
- classification
- input vectors
- linear
- regions
- code
- mathematical equivalents
- perceptron network
- writing
- competitive layer
- competitive neural network
- example
- competitive transfer function <1> <2> <3>
- concurrent inputs <1> <2>
- conjugate gradient algorithm
- Fletcher-Reeves update
- Polak-Ribiere update
- Powell-Beale restarts
- scaled
- continuous stirred tank reactor
- control
- control design
- electromagnet <1> <2>
- feedback linearization <1> <2>
- model predictive control <1> <2> <3> <4> <5> <6>
- model reference control <1> <2> <3> <4> <5> <6>
- NARMA-L2 <1> <2> <3> <4> <5> <6>
- plant <1> <2> <3>
- robot arm
- time horizon
- training data
- CSTR
- custom neural network
- dead neurons
- decision boundary <1> <2>
- definition
- demonstrations
- appelm1
- applin3
- definition
- demohop1
- demohop2
- demorb4
- nnd10lc
- nnd11gn
- nnd12cg
- nnd12m
- nnd12mo
- nnd12sd1 <1> <2>
- nnd12vl
- distance <1> <2>
- box
- custom function
- Euclidean
- link
- Manhattan
- tuning phase
- dynamic networks <1> <2>
- training <1> <2>
- early stopping <1> <2>
- electromagnet <1> <2>
- Elman network
- recurrent connection
- Euclidean distance
- export
- networks <1> <2>
- training data
- feedback linearization <1> <2>
- feedforward network
- finite impulse response filter <1> <2>
- Fletcher-Reeves update
- generalization
- regularization
- generalized regression network
- golden section search
- gradient descent algorithm <1> <2>
- batch
- with momentum <1> <2>
- graphical user interface <1> <2>
- gridtop topology
- Hagan, Martin <1> <2>
- hard limit transfer function <1> <2> <3>
- heuristic techniques
- hidden layer
- definition
- home neuron
- Hopfield network
- architecture
- design equilibrium point
- solution trajectories
- stable equilibrium point
- target equilibrium points
- horizon
- hybrid bisection-cubic search
- import
- networks <1> <2>
- training data <1> <2>
- incremental training
- initial step size function
- initialization
- additional functions
- custom function
- definition
- function
- parameters <1> <2>
- input
- connection
- number
- range
- size
- subobject <1> <2> <3>
- input vector
- outlier
- input vectors
- classification
- dimension reduction
- distance
- topology
- input weights
- definition
- inputs
- concurrent <1> <2>
- sequential <1> <2>
- installation guide
- Jacobian matrix
- Kohonen learning rule
- lambda parameter
- layer
- connection
- dimensions
- distance function
- distances
- initialization function
- net input function
- number
- positions
- size
- subobject
- topology function
- transfer function
- layer weights
- definition
- learning rate
- adaptive
- maximum stable
- optimal
- ordering phase
- too large
- tuning phase
- learning rules
- custom
- Hebb
- Hebb with decay
- instar
- Kohonen
- outstar
- supervised learning
- unsupervised learning
- Widrow-Hoff <1> <2> <3> <4> <5> <6>
- learning vector quantization
- creation
- learning rule <1> <2>
- LVQ network
- subclasses
- target classes
- union of two subclasses
- least mean square error <1> <2>
- Levenberg-Marquardt algorithm
- reduced memory
- line search functions
- backtracking search
- Brent's search
- Charalambous' search
- golden section search
- hybrid bisection-cubic search
- linear networks
- design
- linear transfer function <1> <2> <3> <4>
- linear transfer functions
- linearly dependent vectors
- link distance
- log-sigmoid transfer function <1> <2> <3>
- MADALINE
- magnet <1> <2>
- Manhattan distance
- maximum performance increase
- maximum step size
- mean square error function
- least <1> <2>
- memory reduction
- model predictive control <1> <2> <3> <4> <5> <6>
- model reference control <1> <2> <3> <4> <5> <6>
- momentum constant
- mu parameter
- NARMA-L2 <1> <2> <3> <4> <5> <6>
- neighborhood
- net input function
- custom
- network
- definition
- dynamic <1> <2>
- static
- network function
- network layer
- competitive
- definition
- Network/Data Manager window
- neural network
- adaptive linear <1> <2>
- competitive
- custom
- definition
- feedforward
- generalized regression
- multiple layer <1> <2> <3>
- one layer <1> <2> <3> <4> <5>
- probabilistic
- radial basis
- self organizing
- self-organizing feature map
- Neural Network Design
- Instructor's Manual
- overheads
- neuron
- dead (not allocated)
- definition
- home
- Newton's method
- NN predictive control <1> <2> <3> <4> <5> <6>
- normalization
- inputs and targets
- mean and standard deviation
- notation
- abbreviated <1> <2>
- layer
- transfer function symbols <1> <2>
- numerical optimization
- one step secant algorithm
- ordering phase learning rate
- outlier input vector
- output
- connection
- number
- size
- subobject <1> <2>
- output layer
- definition
- linear
- overdetermined systems
- overfitting
- pass
- definition
- pattern recognition
- perceptron learning rule <1> <2>
- normalized
- perceptron network
- code
- creation
- limitations
- performance function
- custom
- modified
- parameters
- plant <1> <2> <3>
- plant identification <1> <2> <3> <4>
- Polak-Ribiere update
- postprocessing
- post-training analysis
- Powell-Beale restarts
- predictive control <1> <2> <3> <4> <5> <6>
- preprocessing
- principal component analysis
- probabilistic neural network
- design
- quasi-Newton algorithm
- BFGS
- radial basis
- design
- efficient network
- function
- network
- network design
- radial basis transfer function
- recurrent connection
- recurrent networks
- regularization
- automated
- resilient backpropagation
- robot arm
- self-organizing feature map (SOFM) network
- neighborhood
- one-dimensional example
- two-dimensional example
- self-organizing networks
- sequential inputs <1> <2>
- S-function
- sigma parameter
- simulation
- definition
- Simulink
- generating networks
- NNT blockset <1> <2>
- spread constant
- squashing functions
- static networks
- batch training
- training
- subobject
- bias <1> <2> <3>
- input <1> <2> <3> <4>
- layer <1> <2>
- output <1> <2> <3>
- target <1> <2> <3>
- weight <1> <2> <3> <4> <5>
- supervised learning
- target output
- training set
- system identification <1> <2> <3> <4> <5> <6>
- tan-sigmoid transfer function
- tapped delay line <1> <2>
- target
- connection
- number
- size
- subobject <1> <2>
- target output
- time horizon
- topologies
- custom function
- gridtop
- topologies for SOFM neuron locations
- training
- batch <1> <2>
- competitive networks
- custom function
- definition <1> <2>
- efficient
- faster
- function
- incremental
- ordering phase
- parameters
- post-training analysis
- self organizing feature map
- styles
- tuning phase
- training data
- training set
- training styles
- training with noise
- transfer functions
- competitive <1> <2> <3>
- custom
- definition
- derivatives
- hard limit <1> <2>
- linear <1> <2> <3>
- log-sigmoid <1> <2> <3>
- radial basis
- saturating linear
- soft maximum
- tan-sigmoid
- triangular basis
- transformation matrix
- tuning phase learning rate
- tuning phase neighborhood distance
- underdetermined systems
- unsupervised learning
- variable learning rate algorithm
- vectors
- linearly dependent
- weight
- definition
- delays <1> <2>
- initialization function <1> <2>
- learning <1> <2>
- learning function <1> <2>
- learning parameters <1> <2>
- size <1> <2>
- subobject <1> <2> <3>
- value <1> <2> <3>
- weight function <1> <2>
- weight function
- custom
- weight matrix
- definition
- Widrow-Hoff learning rule <1> <2> <3> <4> <5> <6>
- workspace (command line)
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