Neuro-Fuzzy and Soft Computing: inc_ctrst(mf) - File Exchange

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Highlights from
Neuro-Fuzzy and Soft Computing

bellanim.m Animation of two generalized bell membership functions.
bellmanu.m Manual tuning of a generalized bell membership funciton.
fuzpcr(action) FUZPCR Fuzzy printed character recognition.
hedge(action); HEDGE Demonstrate effects of linguistic hedges on fuzzy sets
mlpdm2(trn_data, mlp_config, ... MLPDM2 Demo of solving 2-input 1-output problem using MLP with
noise2.m function out_fismat = noise(mf_type, mf_n, dataset)
percepdm.m Demo of perceptron learning
printui(arg1,arg2,arg3,arg4,a... PRINTUI Print figure with uicontrols.
siganim.m Animation of membership functions composed of two sigmoid functions.
... RBFN Radial basis function networks
... RBFN Radial basis function networks
[ret,x0,str,ts,xts]=kkk(t,x,u... KKK is the M-file description of the SIMULINK system named KKK.
adjeta(eta, rmse) ADJETA Adjust learning rate eta in SD according to history of RMSE.
adjeta(eta, rmse) ADJETA Adjust learning rate eta in SD according to history of RMSE.
adjkappa(kappa, rmse) ADJKAPPA Adjust learning rate eta in SD according to history of RMSE.
arrow(x, y, s, style) ARROW Use arrows to plot curves.
banana(x, y, mode) BANANA Rosenbrock's banana function.
bit2num(bit, range) BIT2NUM Conversion from bit string representations to decimal numbers.
blackbg Change figure background to black
cartmain(all_trn_data, rule_n... CARTMAIN Main routine for CART (regression only)
channel(in0, in1, in2, in3) CHANNEL Channel characteristics of equalization problem.
chap02 List of plots in chapter 2 of "Neuro-Fuzzy and Soft Computing".
chap03 List of plots in chapter 3 of "Neuro-Fuzzy and Soft Computing".
chap04 List of plots in chapter 4 of "Neuro-Fuzzy and Soft Computing".
chap05 List of plots in chapter 5 of "Neuro-Fuzzy and Soft Computing".
chap06 List of plots in chapter 6 of "Neuro-Fuzzy and Soft Computing".
chap07 List of plots in chapter 7 of "Neuro-Fuzzy and Soft Computing".
chap09 List of plots in chapter 9 of "Neuro-Fuzzy and Soft Computing".
chap11 List of plots in chapter 11 of "Neuro-Fuzzy and Soft Computing".
chap12 List of plots in chapter 12 of "Neuro-Fuzzy and Soft Computing".
chap14 List of plots in chapter 14 of "Neuro-Fuzzy and Soft Computing".
chap15 List of plots in chapter 15 of "Neuro-Fuzzy and Soft Computing".
chap17 List of plots in chapter 17 of "Neuro-Fuzzy and Soft Computing".
chap19 List of plots in chapter 19 of "Neuro-Fuzzy and Soft Computing".
cyclesty(H) Cyclesty Cycle line styles.
dec2othe(number, base, digit_... DEC2OTHE Conversion of decimal to other number representation.
defuzzy(x, mf, option) DEFUZZY Defuzzification of MF.
ebr_opti(SNR)
ekfmlp(trn_data, mlp_config, ... SEQMLP On-line extended Kalman filter training for MLP with hyperbolic tangent activation.
emdata(data) EMDATA Error measure of a data set. This is used in CART routine.
emnode(node_index) CARTEM Error measure for a CART node indexed by node_index.
entropy(count) ENTROPY Entropy function, for use as an impurity function in CART.
eqtrain(mf_n, mf_type)
errmoth(para); ERRMOTH Error function for fitting the moth data.
evaleach(string, bit_n, range... EVALEACH Evaluation of each individual's fitness value.
evalpopu(population, bit_n, r... EVALPOPU Evaluation of the population's fitness values.
exts_mf(x, para) EXTSMF S-shaped membership function with two or three parameters
f_de(position, p1, p2, inv_co... F_DE Decision function of channel equalization problem.
fis(x, y, type)
gauss_mf(x, parameter) GAUSS_MF Gaussian membership function with two parameters.
gbell_mf(x, parameter) GBELL_MF Generalized bell-shaped membership function with three parameters.
gd(init_para, x, y)
genfig(figTitle) GENFIG Generates a figure window.
getdata(node_index); GETDATA Get training data of a CART node indexed by node_index.
getdata(node_index, data_inde... SETDATA Set training data of a node in a CART.
gini(count) GINI Gini index function, for use as an impurity function in CART.
growtree GROWTREE Grow tree by splitting a terminal node of a CART.
hwmain(case_n, random_seed) HWMAIN Performance evaluation of MLP learning strategies.
hyperf(x, y, mode) Hyperbolic surface for illustrating steepest descent, Newton,
inc_ctrst(mf) INC_CTRST Contrast intensifier.
invsurf
kfm(dataID) KFM Kohonen's feature map with 2-D output units.
kfm2(dataID) KFM2 Kohonen's feature map with 2-D output units.
list2cb(list)
logmlp(trn_data, mlp_config, ... TANMLP Steepest descent for MLP with hyperbolic tangent activation.
logmlp(trn_data, mlp_config, ... LOGMLP Steepest descent for MLP with logistic activation.
lr_mf(x, parameter) LR_MF Left-right membership function with 3 parameters.
max_star(A, B, star) MAX_STAR returns the max-star composition of given matrices.
mlpdm1(trn_data, mlp_config, ... MLPDM1 Demo of solving 1-input 1-output problem using MLP with
mlpdm1(trn_data, mlp_config, ... MLPDM1 Demo of solving 1-input 1-output problem using MLP with
modmlp(task, mlp_config, trai... MODMLP
modsig(x, parameter) SIG_MF Sigmoidal membership function with two parameters.
nextpopu(popu, fitness, xover...
num2bit(x, range)
paraf(x, y, mode) Parabolic surface for illustrating steepest descent, Newton,
peaksf(x, y, mode) Peaks function for illustrating steepest descent, Newton,
peaksfcn(input)
peaksfcn(input) PEAKSFCN The PEAKS function.
plant(y, u);
plotchar(vec) PLOTCHAR Plots a 35-element vector as a 5x7 grid.
plotrule(cart_table, range, n... PLOTRULE Plot decision boundaries of CART specified by cart_table.
pp PP Pause and prompt.
randsrch(fcn, x, bound, max_e... RANDSRCH Random search method for minimizing a function.
randsrch(fcn, x, bound, max_e... RANDSRCH Random search method for minimizing a function.
seqmlp(trn_data, mlp_config, ... SEQMLP On-line steepest descent for MLP with hyperbolic tangent activation.
sig_mf(x, parameter) SIG_MF Sigmoidal membership function with two parameters.
splitnod(node_index) SPLITNOD Split a CART node indexed by node_index.
splitval(trn_data, col) Find possible split values of a column of a data set. Used in CART.
ssc(a, b, p) SSC Schweizer T-conorm using parameter p.
tanmlp(trn_data, mlp_config, ... TANMLP Steepest descent for MLP with hyperbolic tangent activation.
tanmlp(trn_data, mlp_config, ... TANMLP Steepest descent for MLP with hyperbolic tangent activation.
tanmlp(trn_data, mlp_config, ... TANMLP Steepest descent for MLP with hyperbolic tangent activation
trap_mf(x, parameter) TRAP_MF Trapezoidal membership function with four parameters.
tri_mf(x, parameter) TRI_MF Triangular membership function with three parameters.
trn_1in(mf_n, epoch_n)
trn_2in(mf_n, epoch_n) This script requires the Fuzzy Logic Toolbox from the MathWorks.
trn_3in(mf_n, epoch_n) This script requires the Fuzzy Logic Toolbox from the MathWorks.
tsc(a, b, p) TSC Schweizer T-norm using parameter p.
tsp(loc) TSP Traveling salesman problem (TSP) using SA (simulated annealing).
uniq(in) UNIQ Return a vector where neighboring same elements are reduced to
usecart(data, CART_table) USECART Use a CART tree.
vecdist(mat1, mat2) VECDIST Distance between two set of vectors
wavefcn(x)
activati.m
allbells.m
allfig.m
attract.m
bana_sd.m
bfgs.m
bjpick2.m
bjpick3.m
bjtrain.m
carterr.m
cartglob.m
cartmf.m
cg.m
compball.m
complv.m
contents.m
convex.m
convexmf.m
cooldemo.m
cri.m
cyl_ext.m
decsurf1.m
descent.m
dfp.m
difflr.m
disp_mf.m
disp_sig.m
dryarx.m
drydata.m
drypick.m
drypick2.m
drypick3.m
drypick4.m
drytrain.m
equdata.m
equdec.m
equdensi.m
extensio.m
fcn.m
fitcurve.m
fitmoth.m
fitpeaks.m
ftsurf1.m
ftsurf2.m
fuzimp.m
fuzsetop.m
gdconv.m
gdss1.m
gdss2.m
getauto.m
gmp.m
gmt.m
go_cart.m
go_ga.m
go_icec1.m
go_icec2.m
go_inv.m
go_rand.m
go_rand_.m
go_simp.m
go_simp_.m
hem.m
houdata.m
houpick.m
houpick1.m
houpick2.m
houpick3.m
houpick4.m
houtrain.m
idpick3.m
idpick4.m
impurity.m
init_mf.m
intensif.m
inv_fc.m
inv_sig.m
lingmf.m
lmsaddle.m
loadmpg.m
lv.m
lvqdata.m
mam1.m
mam2.m
mf2d.m
mf_univ.m
mount1.m
mount2.m
mpgdata.m
mpgpick.m
mpgpick2.m
mpgpick3.m
mpgpick4.m
mpgsurf.m
mpgtrain.m
negation.m
newimpur.m
newton.m
nlsebox.m
nndemo.m
noise1.m
nonoise.m
optideci.m
optiplot.m
pchar.m
plot_ebr.m
project.m
rbfndm1.m
rbfnxor.m
rbfnxor1.m
rbfnxor2.m
resolut.m
saddle.m
saddle1.m
saddle2.m
sddemo.m
show_nfc.m
showdata.m
softdemo.m
splits.m
spring.m
spring1.m
sstnorm.m
startup.m
stitch.m
subset.m
sug1.m
sug2.m
taylor.m
tconorm.m
tnorm.m
transfor.m
trn_4in.m
trn_inv.m
tsu1.m
tsurf1.m
tsurf2.m
ttt.m
verify.m
xor2dmf.m
xordata.m
xorfis.m
xormf.m
xorsurf.m
xorsurf1.m
xorsurf2.m
slmg1
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from Neuro-Fuzzy and Soft Computing by Jyh-Shing Roger Jang
Companion Software

inc_ctrst(mf)

function new_mf = inc_ctrst(mf)
% INC_CTRST Contrast intensifier.
%	INC_CTRST(MF) returns a new (array of) membership
%		grades after the contrast intensification operator.

%	Jyh-Shing Roger Jang, 6-2-93.

index1 = find(mf < 0.5);
index2 = find(mf >= 0.5);

tmp = mf(index1);
mf(index1) = 2*tmp.*tmp;
tmp = 1-mf(index2);
mf(index2) = 1-2*tmp.*tmp; 
new_mf = mf;

Highlights from Neuro-Fuzzy and Soft Computing

Highlights from
Neuro-Fuzzy and Soft Computing