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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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