How can I plot this state space like the graph I attached by using tf() and step() command? Thank you! I2/E0=1/(s^3+s^2+3*s+1) NOTE:- Matlabsolutions.com provide latest MatLab Homework Help, MatLab Assignment Help , Finance Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. Try these codes below please; clc; clear; close all; numerator = 1; denominator = [1,1,3,1]; sys = tf(numerator,denominator); yyaxis left SEE COMPLETE ANSWER CLICK THE LINK https://www.matlabsolutions.com/resources/how-to-plot-transfer-functions-in-matlab-.php
I would like to know how the winning neuron is selected by the NEWSOM function within Neural Network Toolbox.
ANSWER
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The NEWSOM function is used to obtain a self-organizing network. The syntax for this function is as follows: net = newsom(PR,[d1,d2,...],tfcn,dfcn,olr,osteps,tlr,tns)
Description
Competitive layers are used to solve classification problems.
NET = NEWSOM(PR,[D1,D2,...],TFCN,DFCN,OLR,OSTEPS,TLR,TNS) takes,
PR - Rx2 matrix of min and max values for R input elements.
Di - Size of ith layer dimension, defaults = [5 8].
TFCN - Topology function, default = 'hextop'.
DFCN - Distance function, default = 'linkdist'.
OLR - Ordering phase learning rate, default = 0.9.
OSTEPS - Ordering phase steps, default = 1000.
TLR - Tuning phase learning rate, default = 0.02;
TND - Tuning phase neighborhood distance, default = 1.
and returns a new self-organizing map.
How the winning neuron is selected:
When an input topology is presented to a SOM network,
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The NEWSOM function is used to obtain a self-organizing network.
The syntax for this function is as follows:
net = newsom(PR,[d1,d2,...],tfcn,dfcn,olr,osteps,tlr,tns) Description Competitive layers are used to solve classification problems. NET = NEWSOM(PR,[D1,D2,...],TFCN,DFCN,OLR,OSTEPS,TLR,TNS) takes, PR - Rx2 matrix of min and max values for R input elements. Di - Size of ith layer dimension, defaults = [5 8]. TFCN - Topology function, default = 'hextop'. DFCN - Distance function, default = 'linkdist'. OLR - Ordering phase learning rate, default = 0.9. OSTEPS - Ordering phase steps, default = 1000. TLR - Tuning phase learning rate, default = 0.02; TND - Tuning phase neighborhood distance, default = 1. and returns a new self-organizing map.
How the winning neuron is selected:
When an input topology is presented to a SOM network,
SEE COMPLETE ANSWER CLICK THE LINK
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