模糊神经网络（FNN）用于数据预测（Python源码+数据集）

#库的导入 import numpy as np import pandas as pd #输入数据的导入 df = pd.read_csv("train.csv") df.columns = ["Co", "Cr", "Mg", "Pb", "Ti"] Co = df["Co"] Co = np.array(Co) Cr = df["Cr"] Cr = np.array(Cr) Mg=df["Mg"] Mg=np.array(Mg) Pb = df["Pb"] Pb =np.array(Pb) Ti = df["Ti"] Ti = np.array(Ti) samplein = np.mat([Co,Cr,Mg,Pb]) #数据归一化，将输入数据压缩至0到1之间，便于计算，后续通过反归一化恢复原始值 sampleinminmax = np.array([samplein.min(axis=1).T.tolist()[0],samplein.max(axis=1).T.tolist()[0]]).transpose()#对应最大值最小值 #待预测数据为Ti sampleout = np.mat([Ti]) sampleoutminmax = np.array([sampleout.min(axis=1).T.tolist()[0],sampleout.max(axis=1).T.tolist()[0]]).transpose() sampleinnorm = ((np.array(samplein.T)-sampleinminmax.transpose()[0])/(sampleinminmax.transpose()[1]-sampleinminmax.transpose()[0])).transpose() sampleoutnorm = ((np.array(sampleout.T)-sampleoutminmax.transpose()[0])/(sampleoutminmax.transpose()[1]-sampleoutminmax.transpose()[0])).transpose() sampleinnorm = sampleinnorm.transpose() sampleoutnorm = sampleoutnorm.transpose() S = 2 #模糊分级个数 T = 16 #规则生成层、归一化层节点数 iteration = 500 #迭代训练次数 learningrate = 0.01 #学习率 #c1、b1分别为李隶属度层的中心点与宽度向量、w1为输出层的权值，sampleinnorm.shape[1]代表输入数据的4个类别 c1 = np.random.uniform(low=-1, high=1, size=(S, sampleinnorm.shape[1])) b1 = np.random.uniform(low=-1, high=1, size=(S, sampleinnorm.shape[1])) w1 = np.random.uniform(low=0, high=1, size=(1, T)) sampleinnorm = np.mat(sampleinnorm) #开始训练 for l in range(iteration): print("the iteration is :",l+1) # 隶属度函数计算层计算输出 Y = sampleoutnorm.copy() u1 = np.zeros((S, sampleinnorm.shape[1])) alpha = [] alpha2 = [] a1 = [] for m in range(sampleinnorm.shape[0]): for i in range(S): for j in range(sampleinnorm.shape[1]): u1[i][j] = np.exp((-1) * ((sampleinnorm[m, j] - c1[i][j]) ** 2) / (b1[i][j] ** 2)) # 规则生成层计算输出 alpha1 = np.zeros((T, 1)) for i in range(S): a = 1 if i == 1: a = 8 for p in range(S): b = 1 if p == 1: b = 4 for q in range(S): c = 1 if q == 1: c = 2 for k in range(S): alpha1[i * a + p * b + q * c + k] = u1[i][0] * u1[p][1] * u1[q][2] * u1[k][3] alpha.append(alpha1) # 归一化层计算输出 alphasum = np.sum(alpha1) alpha2.append(alphasum) # 输出层计算输出 a2 = np.dot(w1,alpha1) Y[m] = a2/alphasum #计算误差 err = sampleoutnorm - Y loss = np.sum(np.abs(err)) print(" the loss is :",loss) #反向传播，分别计算参数w1、c1、b1的误差项 deltaw1 = np.zeros((1,T)) deltac1 = np.zeros((S, sampleinnorm.shape[1])) deltab1 = np.zeros((S, sampleinnorm.shape[1])) for m in range(sampleinnorm.shape[0]): changew1 = ((err[m] * alpha[m])/alpha2[m]).transpose() changec2 = np.zeros((S, sampleinnorm.shape[1])) changeb2 = np.zeros((S, sampleinnorm.shape[1])) z=np.zeros((S, sampleinnorm.shape[1])) v=np.zeros((S, sampleinnorm.shape[1])) for i in range(S): for j in range(sampleinnorm.shape[1]): z[i][j]=2*(sampleinnorm[m, j] - c1[i][j])/(b1[i][j] ** 2) v[i][j]=2*((sampleinnorm[m, j] - c1[i][j])**2)/(b1[i][j]**3) h = alpha[m] for u in range(T): changec1 = (((w1[:,u]-Y[m])*h[u,:])/alpha2[m])*z changec2 = changec1+changec2 changeb1 = (((w1[:,u]-Y[m])*h[u,:])/alpha2[m])*v changeb2 = changeb1+changeb2 deltac1 = deltac1 + changec2 deltab1 = deltab1 + changeb2 deltaw1 = deltaw1 + changew1 #对三个参数进行更新 c1 = c1 + learningrate * deltac1 b1 = b1 + learningrate * deltab1 w1 = w1 + learningrate * deltaw1 print('更新的w1:',w1) print('更新的b1:',b1) print('更新的c1:',c1) #保存训练后的参数 np.save("c1.npy",c1) np.save("b1.npy",b1) np.save("w1.npy",w1)