pso優(yōu)化bp算法python代碼

1-pso優(yōu)化bp算法python代碼PSO優(yōu)化BP算法Python代碼是一種利用粒子群優(yōu)化算法優(yōu)化BP算法的Python代碼。通過將粒子群優(yōu)化算法與BP算法相結合，可以更好地解決BP算法在訓練過程中容易陷入局部最優(yōu)解的問題，從而提高模型的準確性和收斂速度。以下是一個簡單的PSO優(yōu)化BP算法Python代碼示例：```pythonimportnumpyasnpimportrandom#定義BP神經(jīng)網(wǎng)絡類classBPNN:def__init__(self,n_input,n_hidden,n_output):self.n_input=n_inputself.n_hidden=n_hiddenself.n_output=n_outputself.w1=np.random.rand(n_input,n_hidden)self.b1=np.random.rand(n_hidden)self.w2=np.random.rand(n_hidden,n_output)self.b2=np.random.rand(n_output)defsigmoid(self,x):return1.0/(1.0+np.exp(-x))defforward(self,x):y1=np.dot(x,self.w1)+self.b1z1=self.sigmoid(y1)y2=np.dot(z1,self.w2)+self.b2z2=self.sigmoid(y2)returnz2defpredict(self,X):Y=np.zeros((X.shape[0],self.n_output))foriinrange(X.shape[0]):Y[i]=self.forward(X[i])returnY#定義粒子類classParticle:def__init__(self,dim):self.position=np.random.rand(dim)self.velocity=np.random.rand(dim)self.fitness=float('inf')self.best_position=self.position.copy()self.best_fitness=float('inf')defupdate_fitness(self,fitness):self.fitness=fitnessiffitness<self.best_fitness:self.best_fitness=fitnessself.best_position=self.position.copy()#定義粒子群優(yōu)化算法類classPSO:def__init__(self,func,dim,n_particles,max_iter,lb,ub,w=0.729,c1=1.49445,c2=1.49445):self.func=funcself.dim=dimself.n_particles=n_particlesself.max_iter=max_iterself.lb=lbself.ub=ubself.w=wself.c1=c1self.c2=c2self.particles=[Particle(dim)foriinrange(n_particles)]self.gbest_position=np.zeros(dim)self.gbest_fitness=float('inf')defoptimize(self):foriinrange(self.max_iter):forjinrange(self.n_particles):#更新速度和位置self.particles[j].velocity=self.w*self.particles[j].velocity+self.c1*random.random()*(self.particles[j].best_position-self.particles[j].position)+self.c2*random.random()*(self.gbest_position-self.particles[j].position)self.particles[j].position+=self.particles[j].velocity#邊界處理self.particles[j].position[self.particles[j].position<self.lb]=self.lbself.particles[j].position[self.particles[j].position>self.ub]=self.ub#計算適應度fitness=self.func(self.particles[j].position)#更新個體最優(yōu)解和全局最優(yōu)解self.particles[j].update_fitness(fitness)iffitness<self.gbest_fitness:self.gbest_fitness=fitnessself.gbest_position=self.particles[j].position.copy()#定義損失函數(shù)defloss_function(theta,X,Y):n_input,n_hidden,n_output=2,3,1nn=BPNN(n_input,n_hidden,n_output)nn.w1=theta[0:6].reshape(n_input,n_hidden)nn.b1=theta[6:9].reshape(n_hidden)nn.w2=theta[9:12].reshape(n_hidden,n_output)nn.b2=theta[12:].reshape(n_output)Y_pred=nn.predict(X)returnnp.mean((Y_pred-Y)**2)#生成數(shù)據(jù)X=np.array([[0,0],[0,1],[1,0],[1,1]])Y=np.array([[0],[1],[1],[0]])#定義搜索范圍lb=np.array([-5]*12)ub=np.array([5]*12)#定義PSO算法pso=PSO(loss_function,dim=12,n_particles=20,max_iter=100,lb=lb,ub=ub)#運行PSO算法pso.optimize()#輸出結果print('Globalbestfitness:',pso.gbest_fitness)print('Globalbestposition:',pso.gbest_position)#計算模型預測結果theta=pso.gbest_positionn_input,n_hidden,n_output=2,3,1nn=BPNN(n_input,n_hidden,n_output)nn.w1=theta[0:6].reshape(n_input,n_hidden)nn.b1=theta[6:9].reshape(n_hidden)nn.w2=theta[9:12].reshape(n_hidd