A First Course in FUZZY and NEURAL CONTROL

5.7. EXAMPLE 2: TRAINING A NEURAL NETWORK 191 0 50 100 150 200 250 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Bark Stick Leaf Pepper Fi ...

192 CHAPTER 5. NEURAL NETWORKS FOR CONTROL 5.8 Practicalissuesintraining...................... There is no prescribed methodolog ...

5.9. EXERCISES AND PROJECTS 193 What is a good size for the training set? As in statistics, such a question only makes sense wh ...

194 CHAPTER 5. NEURAL NETWORKS FOR CONTROL Consider the following training set consisting of bipolar input-output pairs {(xq,yq ...

5.9. EXERCISES AND PROJECTS 195 (c) Using the training and test data obtained in (a) and (b), retrain and test a single-hidden-l ...

196 CHAPTER 5. NEURAL NETWORKS FOR CONTROL imaging the street-crossing signal. A real-time neural network processor would then i ...

5.9. EXERCISES AND PROJECTS 197 existingfile? Yes or Noî such a response would have to be in Braille for the operator to choose ...

198 CHAPTER 5. NEURAL NETWORKS FOR CONTROL synthesis and chlorophyll production. Unhealthy plants, however, reflect most of the ...

5.9. EXERCISES AND PROJECTS 199 Results from a trained neural network can be compared with the results from MultiSpec. ïFor the ...

Chapter 6 NEURAL CONTROL Neural control refers both to a methodology in which the controller itself is a neural network, and to ...

202 CHAPTER 6. NEURAL CONTROL for adaptive control where controllers need to adapt to changing environment, such as for time-var ...

6.2. INVERSE DYNAMICS 203 x(k+3) = Ax(k+2)+Bu(k+2) = A ° A^2 x(k)+ABu(k)+Bu(k+1) ¢ +Bu(k+2) = A^3 x(k)+A^2 Bu(k)+ABu(k+1)+Bu(k+2 ...

204 CHAPTER 6. NEURAL CONTROL When the inverse dynamics of a plant exist, one can try to control the plant by modeling its inver ...

6.4. EXAMPLE: TEMPERATURE CONTROL 205 6.4.1 A neural network for temperature control Wefirst demonstrate a very simple neural ne ...

206 CHAPTER 6. NEURAL CONTROL p(i)=(exp(-time))*sin(w*time); time=time+0.1; if (p(i)>0.0) t(i)=1; elseif (p(i)<0.0) t(i)=- ...

6.4. EXAMPLE: TEMPERATURE CONTROL 207 the number of outputs we wish to generate. For example, in a system with one binary output ...

208 CHAPTER 6. NEURAL CONTROL -1-0 .6 -0 .4 -0 .2 0 0.2 0.4 0.6 0.8 -0 .5 0 0.5 1 1.5 Figure 6.5. Original functionëoí versus tr ...

6.4. EXAMPLE: TEMPERATURE CONTROL 209 We are now in a position to test the performance of the neural network to provide ON/OFF c ...

210 CHAPTER 6. NEURAL CONTROL time=time+0.1; end We obtain a plot of the generated training pattern vector as shown in Figure 6. ...

6.4. EXAMPLE: TEMPERATURE CONTROL 211 Plots of the proportional gain versus thegradient and integral gain versus the pattern vec ...