模糊推理系統

Question

我想通過使用evalfis（）函數給iput使用.fis文件。我給輸入形式：

 jo= myImage(2); % jo contains the graysacle value of pixel at location 2   

     a=readfis('terrain_classification_fuzzy'); 
     Result = evalfis(jo,a); 

但我得到以下錯誤：

???使用錯誤==> evalfismex 第一個輸入必須是一個定義DOUBLE矩陣

在==誤差>在84 [輸出，IRR，ORR，ARR] = evalfismex（輸入，FIS，numofpoints）evalfis;

錯誤==> see_movie_segmentationTry4_incorrect at 113 Result = evalfis（[jo]，a）;

請幫我這...

Answer 1

我發現的錯誤。這只是「jo」初始設置爲int8而不是double類型。

Answer 2

檢查這個例子中，參數，看看你是否有類似的參數：

function trn_2in(mf_n, epoch_n)  
% This script requires the Fuzzy Logic Toolbox from the MathWorks. 
if nargin < 2, epoch_n = 100; end;  
if nargin < 1, mf_n = 4; end;  
% ====== collect training data  
point_n = 11;  
x = linspace(-10, 10, point_n);  
y = linspace(-10, 10, point_n);  
[xx, yy] = meshgrid(x, y);  
tmp1 = sin(xx)./(xx);  
index = find(isnan(tmp1)==1);  
tmp1(index) = ones(size(index)); 
tmp2 = sin(yy)./(yy);  
index = find(isnan(tmp2)==1);  
tmp2(index) = ones(size(index)); 
zz = tmp1.*tmp2;  
trn_data = [xx(:) yy(:) zz(:)];  
% ====== training options  
ss = 0.1;  
ss_dec_rate = 0.9;  
ss_inc_rate = 1.1; 
mf_type = 'gbellmf'; 
% ====== generate the initial FIS  
in_fismat = genfis1(trn_data, mf_n, mf_type); 
% ====== start training  
[trn_out_fismat trn_error step_size] = ...  
    anfis(trn_data, in_fismat, [epoch_n nan ss ss_dec_rate ss_inc_rate]);  
% ====== compute ANFIS output  
z_hat = evalfis([xx(:) yy(:)], trn_out_fismat); 
% ====== plot of training data 
genfig('training data');  
subplot(221);  
mesh(xx, yy, zz);  
limit = [min(xx(:)) max(xx(:)) min(yy(:)) max(yy(:)) ...  
    min(zz(:)) max(zz(:))]; 
    axis(limit); set(gca, 'box', 'on');  
xlabel('X'); ylabel('Y'); title('Training data');  
    zz_hat = evalfis([xx(:) yy(:)], trn_out_fismat);  
subplot(222);  
mesh(xx, yy, reshape(zz_hat, point_n, point_n));  
axis(limit); set(gca, 'box', 'on');  
xlabel('X'); ylabel('Y'); title('ANFIS Output');  
subplot(223)  
plot(1:epoch_n, trn_error); 
xlabel('epoch number'); ylabel('root mean squared error');  
title('error curve'); 
subplot(224)  
plot(1:epoch_n, step_size);  
xlabel('epoch number'); ylabel('step size');  
title('step size curve');  
% ====== plot MFs  
figH = genfig('MFs for SINC function training');  
% plot initial MFs on x and y  
subplot(221); plotmf(in_fismat, 'input', 1);  
subplot(222); plotmf(in_fismat, 'input', 2);  
subplot(223); plotmf(trn_out_fismat, 'input', 1); 
subplot(224); plotmf(trn_out_fismat, 'input', 2);  
delete(findobj(figH, 'type', 'text'));  
subplot(221); title('Initial MFs on X');  
subplot(222); title('Initial MFs on Y');  
subplot(223); title('Final MFs on X');  
subplot(224); title('Final MFs on Y');