LightGBM-BPNN modelorange juice futures pricesmachine learningcommoditiesSHAP interpretabilityForecasting financial assets in volatile markets, like orange juice futures (OJ=F), poses challenges due to complex price movements. This study introduces a hybrid model that combines a Light Gradient Boosting ...
假设我们已经训练了一个 BPNN 模型并将其保存为 model.h5 文件。以下是如何在 Python 中加载模型并进行预测的示例代码: 4.1 使用 Keras 加载模型 AI检测代码解析 import numpy as np from keras.models import load_model # 1. 加载模型 model = load_model('model.h5') # 2. 准备输入数据 # 假设我们的模...
关键词:遗传算法;BP 神经网络;插值;降雨量;分布式 中图分类号:P 332.1 文献标志码:A Application of BPNN model optimized by GA to distributed rainfall interpolation H U Guangyi,ZH ANG Qiuw en,ZH ANG Yongchuan (Co llege of H y dr oelectr icity and Dig italization Engineer ing ,H ua...
bpnn的意思是：神经网络 ; 反向传播神经网络 ; 误差反向传播神经网络。双语例句：1、Black body furnace temperature time series prediction model based on BPNN was built.文章在神经网络的基础上，建立了黑体炉温度时序预测模型。2、Efficiency parameters optimization about terminal-sensitive projectiles w...
Title:Performance Study of the MPC based on BPNN Prediction Model in Thermal Management System of Battery Electric Vehicles 题目:基于 BPNN 预测模型的 MPC 在纯电动汽车热管理系统中的性能研究 Authors:HE Lian’ge, JING Haodong, ...
self.biases=[]foriinrange(len(model) - 1):#矩阵个数为总层数减1,例如4层的网络只需要3个矩阵就可以了self.matrixs.append(np.random.randn(model[i], model[i + 1]))#矩阵的列数是对应输入节点的个数,矩阵的行数对应输出节点的个数foriinrange(len(model) - 1):#列表中的每个np数组代表一整层...
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The prediction results show that the ALO-BPNN is a satisfactory prediction model and achieves excellent prediction accuracy in terms of four performance indices (the determination coefficient (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the variance accounted for (...
x = self.fc(x)returnx# 实例化卷积神经网络模型model = CNN()# 定义损失函数和优化器criterion = nn.CrossEntropyLoss()#lr(学习率)是控制每次更新的参数的大小的超参数optimizer = torch.optim.Adam(model.parameters(), lr=0.01)# 训练模型forepochinrange(1):fori, (images, labels)inenumerate(train_...
构建BPNN模型model=Sequential()model.add(Dense(128,activation='relu',input_shape=(28*28,)))model.add(Dense(64,activation='relu'))model.add(Dense(10,activation='softmax'))# 编译模型model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])# 训练模型history=model.fit...