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psort-in-matlab.zip （11个子文件）

license.txt 1KB

PSORT-NN_addin20101128i

PSO_stag_count.m 569B

README.pdf 365KB

update_tr.m 808B

plot_epochs.m 1KB

nn_train_feedback.m 2KB

display_NN_settings.m 764B

display_NN_results.m 653B

trainpso.m 5KB

NN_training_demo.m 1KB

plotPSO_particles.m 2KB

Tricia Rambharose README: NN add-in for PSORT Page 1 of 8

Artificial Neural Network training Add-in

for

Particle Swarm Optimization Research Toolbox

Version 20101128i

By Tricia Rambharose

tricia.rambharose@sta.uwi.edu

Table of Contents

Introduction ..................................................................................................................... 2

Steps to implement this NN add-in .................................................................................. 2

Functions ......................................................................................................................... 3

main.m ......................................................................................................................... 3

trainpso.m .................................................................................................................... 3

first_pso ................................................................................................................... 3

plot_epochs.m .............................................................................................................. 3

plotPSO_particles.m .................................................................................................... 4

display_NN_results.m .................................................................................................. 5

display_NN_settings.m ................................................................................................ 5

ObjFun_NN.m .............................................................................................................. 5

Changes made to PSORT functions ............................................................................... 6

Control_Panel.m .......................................................................................................... 6

Display_Settings.m ...................................................................................................... 6

Objectives.m ................................................................................................................ 6

Ethics.m ....................................................................................................................... 6

RegPSO_main.m ......................................................................................................... 6

lbest_core.m and gbest_core.m ................................................................................... 6

Support for users ............................................................................................................. 7

References ...................................................................................................................... 8

Tricia Rambharose README: NN add-in for PSORT Page 2 of 8

Introduction

This add-in to the PSO Research toolbox (Evers 2009) aims to allow an artificial neural

network (ANN or simply NN) to be trained using the Particle Swarm Optimization (PSO)

technique (Kennedy, Eberhart et al. 2001).

This add-in acts like a bridge or interface between MATLAB‟s NN toolbox and the PSO

Research Toolbox. In this way, MATLAB‟s NN functions can call the NN add-in, which in

turn calls the PSO Research toolbox for NN training.

This approach to training a NN by PSO treats each PSO particle as one possible

solution of weight and bias combinations for the NN (Settles and Rylander ; Rui Mendes

2002; Venayagamoorthy 2003). The PSO particles therefore move about in the search

space aiming to minimise the output of the NN performance function.

Steps to implement this NN add-in

1. Download the most recent version of the PSO Research Toolbox.

2. Download the most recent version of the NN add-in and unzip it alongside the PSO

Research Toolbox folder named PSORTyyyymmdd.

3. On the MATLAB interface go to file -> set path -> add with subfolders. Then find the

location of the add-in folder and click OK

4. In the PSORT open Control_Panel.m and set the following switches:

OnOff_Tricias_NN_training = logical(1);

Note: This is the first switch in the control panel.

OnOff_user_input_validation_required = logical(0);

Note: This switch can be found in the control panel‟s section (1) BASIC

SWITCHES & PSO ALGORITHM SELECTION under heading

MISCELLANEOUS FEATURES.

5. Execute the add-in file NN_training_demo.m by one of the following ways:

5.1. Opening NN_training_demo.m pressing F5 on the keyboard

5.2. Clicking on the button found at the top of the MATLAB editor window of

NN_training_demo.m

5.3. Typing NN_training_demo.m on the MATLAB workspace and pressing enter.

NN settings can easily be modified in the body of the NN_training_demo file and PSO

parameters can be modified in “Control_Panel.m” of the PSORT.

Tricia Rambharose README: NN add-in for PSORT Page 3 of 8

Functions

This section describes the add-in functions created for NN training with PSO.

main.m

This is a simple function used as an example of creating a neural network (NN) and

using the PSO algorithm as the training function. Very basic input and target values are

used, similar to the examples given in MATLAB‟s Neural Network toolbox help file. A

two layer NN is created and NN parameters are set. NN parameters mostly adjusted for

testing and therefore set in main are: the NN training function, transfer functions, goal,

max epochs, max_fail and a new PSO plot function. The NN is first simulated and then

trained. For comparative analysis of results, the NN settings and results are displayed

by calling respective functions created for this purpose. Finally, a function is called to

plot the NN performance results vs. epochs to enable more meaningful analysis of

results.

trainpso.m

This is a modification of existing neural network training algorithms provided in Matlab's

Neural Network toolbox, using ideas from Brian Birge's PSO toolbox (Birge 2005),

created for MATLAB 2005. This PSO training function aims to be the interface between

the Matlab NN toolbox and the PSO Research Toolbox. For consistency, the formatting

and structure of other NN training algorithms are followed. Training here is based on the

idea that all weight and bias values are determined using a swarm optimization

approach.

Further explanations of variables and steps in this function are given in the

documentation of the function file.

first_pso

This variable is in the trainpso.m function as a flag to indicate to the PSO toolbox the

first time the PSO algorithm is run. This is necessary to prevent redundant PSO

parameter validation and display that is needed only if the PSORT is not being used for

NN training. Initially, the value of this variable is set to „1‟ and after the first PSO trial its

value is automatically set to „0‟ in RegPSO_main.m.

plot_epochs.m

This is a script to plot NN epochs versus performance at the end of the NN training

session. NN Epoch number is given on the x-axis and the NN performance is given on

the y-axis. For PSO training of a NN, each PSO iteration corresponds to a NN epoch.

For added analysis, the NN training goal is shown as a horizontal line. Figure 1 below

gives a screenshot of the plot generated from plot_epochs.m.

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