Backgound in Electromagnetic Theory, Plasma Physics and Radar Target Identification using Neural Networks.

PhD Student, Research Assistant and Lecturer at Stanford;

AB, ScB, ScM Student; Research Assistant, Fellow and Professor at Brown;

27 yrs researching Ballistic and Theatre Missile Defense using Neural Networks at MIT Lincoln Laboratory. Retired 2003.

PLEASE DO NOT SEND QUESTIONS AND DATA TO MY EMAIL. HOWEVER, CAN SEND LINKS TO POSTS.

Professional Interests: Neural Netwoks, Spectral Analysis

Answered

How to disable validation and test data set in neural network

You have to define net before modifying any properties. clear all, close all, clc [x,t] = iris_dataset; for i = 1:2 net ...

How to disable validation and test data set in neural network

You have to define net before modifying any properties. clear all, close all, clc [x,t] = iris_dataset; for i = 1:2 net ...

3 mois ago | 1

Answered

Training data and Training target in Neural Networks

You cannot make any intelligent decisions until you have examined a plot of the data!!! (WRONG!!! Plotting the data first is ...

Training data and Training target in Neural Networks

You cannot make any intelligent decisions until you have examined a plot of the data!!! (WRONG!!! Plotting the data first is ...

4 mois ago | 0

| accepted

Answered

simulink neural network producing different outputs to workspace

A simpler solution is to ALWAYS begin the program with a resetting of the random number generator. For example, choose your favo...

simulink neural network producing different outputs to workspace

A simpler solution is to ALWAYS begin the program with a resetting of the random number generator. For example, choose your favo...

4 mois ago | 1

Answered

How to avoid getting negative values when training a neural network?

Use a sigmoid for the output layer. Hope this helps THANK YOU FOR FORMALLY ACCEPTING MY ANSWER GREG

How to avoid getting negative values when training a neural network?

Use a sigmoid for the output layer. Hope this helps THANK YOU FOR FORMALLY ACCEPTING MY ANSWER GREG

4 mois ago | 0

Answered

weird plotregression plots for a 10% of my fitnet neural networks

Sometimes training gets into a parameter space rut. That is why it is wise to train multiple models. Hope this helps. Greg ...

weird plotregression plots for a 10% of my fitnet neural networks

Sometimes training gets into a parameter space rut. That is why it is wise to train multiple models. Hope this helps. Greg ...

4 mois ago | 0

Answered

What is the purpose of shuffling the validation set?

To impose and verify a consistent GENERALIZED path to convergence by avoiding repetitive anomalies. Hope this helps Greg

What is the purpose of shuffling the validation set?

To impose and verify a consistent GENERALIZED path to convergence by avoiding repetitive anomalies. Hope this helps Greg

4 mois ago | 0

Answered

How to force overfiting of Deep Learning Network for Classification

OVERFITTING = More training unknowns (e.g., weights) than training vectors. OVERTRAINING1 = Training an overfit network to...

How to force overfiting of Deep Learning Network for Classification

OVERFITTING = More training unknowns (e.g., weights) than training vectors. OVERTRAINING1 = Training an overfit network to...

4 mois ago | 0

Answered

Neural Network Pattern Recognition

Targets are 1-dimensional unit vectors with 4 zeros and a single 1 . Thank you for formally accepting my answer. Greg

Neural Network Pattern Recognition

Targets are 1-dimensional unit vectors with 4 zeros and a single 1 . Thank you for formally accepting my answer. Greg

4 mois ago | 0

| accepted

Answered

semanticseg producing marginally different values when inference is repeated

Use clear all, close all, clc, rng(0) on the 1st line Thank you for formally accepting my answer Greg

semanticseg producing marginally different values when inference is repeated

Use clear all, close all, clc, rng(0) on the 1st line Thank you for formally accepting my answer Greg

4 mois ago | 0

Answered

combining two neural networks in one bigger network

Just 1. Save the outputs of net1 in a file 2. Use the file to train net2 Greg

combining two neural networks in one bigger network

Just 1. Save the outputs of net1 in a file 2. Use the file to train net2 Greg

4 mois ago | 0

Answered

Problem with the TreeBagger Command

The sizes of the input function and output target must be [ I N ] = size(input) [ O N ] = size (target) Hope this helps...

Problem with the TreeBagger Command

The sizes of the input function and output target must be [ I N ] = size(input) [ O N ] = size (target) Hope this helps...

5 mois ago | 0

Answered

How do I create a neural network that will give multiple input and outputs?

ALWAYS arrange your data so that [ I N ] = size(input) [ O N ] = size(output) Hope this helps. Greg

How do I create a neural network that will give multiple input and outputs?

ALWAYS arrange your data so that [ I N ] = size(input) [ O N ] = size(output) Hope this helps. Greg

5 mois ago | 0

Answered

How to make a hybrid model (LSTM and Ensemble) in MATLAB

Replace your YES/NO data with either 1/0 or 1/-1. Hope this helps. Greg

How to make a hybrid model (LSTM and Ensemble) in MATLAB

Replace your YES/NO data with either 1/0 or 1/-1. Hope this helps. Greg

5 mois ago | 1

Answered

how to augment image data only for a specific class?

Separate class 0 and interpolate. If you have a good feel for the data you could extrapolate. However the latter might be tricky...

how to augment image data only for a specific class?

Separate class 0 and interpolate. If you have a good feel for the data you could extrapolate. However the latter might be tricky...

5 mois ago | 0

Answered

NTSTOOL - How to get predicted values of "the future"?

The known time series is analyzed to yield a time-series model that uses past and present values to predict future values. The ...

NTSTOOL - How to get predicted values of "the future"?

The known time series is analyzed to yield a time-series model that uses past and present values to predict future values. The ...

8 mois ago | 0

Answered

Timedelaynet output calculation principle

You did not include tHe 2 biases. Hope this helps. Greg THANK YOU FOR FORMALLY ACCEPTING MY ANSWER

Timedelaynet output calculation principle

You did not include tHe 2 biases. Hope this helps. Greg THANK YOU FOR FORMALLY ACCEPTING MY ANSWER

9 mois ago | 0

Answered

Understand number of weights of Neural Network

It is possible. In general, however, you don't have the slightest idea what choice would be significantly better than random. ...

Understand number of weights of Neural Network

It is possible. In general, however, you don't have the slightest idea what choice would be significantly better than random. ...

9 mois ago | 0

Answered

Using pca for features selections

PCA (Principal Coordinate Analysis) is a very useful method for regression (it ranks linear combinations of the original variabl...

Using pca for features selections

PCA (Principal Coordinate Analysis) is a very useful method for regression (it ranks linear combinations of the original variabl...

9 mois ago | 0

Answered

How do we decide the number of hiddenlayers in a PatternNet?

patternnet(10) indicates ONE HIDDEN LAYER WITH TEN NODES It is important to be mindful of the number of layers and nodes. The ...

How do we decide the number of hiddenlayers in a PatternNet?

patternnet(10) indicates ONE HIDDEN LAYER WITH TEN NODES It is important to be mindful of the number of layers and nodes. The ...

10 mois ago | 0

| accepted

Answered

Why sets Matlab automatically the activation functions for a neural network like this?

The simplest useful approximation is is a series of blocks with different heights and widths. The simplest useful DIFFERENTIAB...

Why sets Matlab automatically the activation functions for a neural network like this?

The simplest useful approximation is is a series of blocks with different heights and widths. The simplest useful DIFFERENTIAB...

10 mois ago | 0

Answered

Artificial Neural Networks Hidden Layers

Number of input and output nodes is determined by the data. Number of hidden layers and nodes is determined by the program auth...

Artificial Neural Networks Hidden Layers

Number of input and output nodes is determined by the data. Number of hidden layers and nodes is determined by the program auth...

10 mois ago | 0

| accepted

Answered

Neural Network Classification Results

The original class sizes are unequal. Hope this helps THANK YOU FOR FORMALLY ACEEPTING MY ANSWER Greg

Neural Network Classification Results

The original class sizes are unequal. Hope this helps THANK YOU FOR FORMALLY ACEEPTING MY ANSWER Greg

10 mois ago | 0

Answered

Please help with narnext error Subscripted assignment dimension mismatch.????

x5=data_inputs(5,1:17); x6=data_inputs(6,1:17); x7=data_inputs(6,1:17); x8=data_inputs(7,1:17); x9=data_inputs(9,1:17); Hop...

Please help with narnext error Subscripted assignment dimension mismatch.????

x5=data_inputs(5,1:17); x6=data_inputs(6,1:17); x7=data_inputs(6,1:17); x8=data_inputs(7,1:17); x9=data_inputs(9,1:17); Hop...

10 mois ago | 0

Answered

Hyperparameter tuning of neural network

One hidden layer is always sufficient. However, sometimes 1. Knowledge of the physical or mathematical process may lead to a ...

Hyperparameter tuning of neural network

One hidden layer is always sufficient. However, sometimes 1. Knowledge of the physical or mathematical process may lead to a ...

10 mois ago | 1

Answered

Different results in training a CNN with Matlab 2018a and Matlab 2019a

You are making the task difficult by going backwards. Start with a single hidden node and add nodes one at a time. Hope this...

Different results in training a CNN with Matlab 2018a and Matlab 2019a

You are making the task difficult by going backwards. Start with a single hidden node and add nodes one at a time. Hope this...

10 mois ago | 0

Question

NEURAL NETWORK DATA SET EXAMPLES

For demonstration of old AND new concepts and ideas, PLEASE use the sample NN data sets provided by MATLAB help nndatas...

10 mois ago | 0 answers | 0

Answered

cross validation in neural network using K-fold

%i am using neural network for classification but i need to use instead of holdout option , K-fold. ==> FALSE!. You mean y...

cross validation in neural network using K-fold

%i am using neural network for classification but i need to use instead of holdout option , K-fold. ==> FALSE!. You mean y...

10 mois ago | 0

Answered

Can the number of Predictors be different for Train and Test data?

Of course not. The ultimate purpose of training is to create a model that works well on non-training data. Thank you for form...

Can the number of Predictors be different for Train and Test data?

Of course not. The ultimate purpose of training is to create a model that works well on non-training data. Thank you for form...

10 mois ago | 0

Answered

How to check the robustness of the Neural network model?

If you are going to test with white noise, include white noise in your design (i.e., training + validation) Then, given a fixed...

How to check the robustness of the Neural network model?

If you are going to test with white noise, include white noise in your design (i.e., training + validation) Then, given a fixed...

11 mois ago | 0

| accepted

Answered

NARX with Complex Values Input

Decades ago I learned (the hard way) to forget about trying to use complex computations for NNs. However, if you insist, let us...

NARX with Complex Values Input

Decades ago I learned (the hard way) to forget about trying to use complex computations for NNs. However, if you insist, let us...

11 mois ago | 1