python实现WOA-CNN-LSTM-Attention

### 实现WOA-CNN-LSTM-Attention模型 对于多变量时间序列预测，WOA-CNN-LSTM-Attention模型融合了多种技术的优势来提高预测精度。该模型通过鲸鱼优化算法(WOA)[^1]调整超参数并优化LSTM网络结构；利用卷积神经网络(CNN)提取局部特征；采用注意力机制(Attention)聚焦于重要信息。 #### Python实现框架搭建 为了构建此复杂架构，在Python环境中需安装必要的库： ```bash pip install numpy pandas tensorflow scikit_learn matplotlib seaborn ``` 接着定义各组件函数与类： ```python import numpy as np from keras.models import Model from keras.layers import Input, Dense, Conv1D, MaxPooling1D, Flatten, LSTM, concatenate, Attention from sklearn.preprocessing import MinMaxScaler from whaloa import WhaleOptimizationAlgorithm # 假设已有一个可用的WOA包 def create_cnn_model(input_shape): inputs = Input(shape=input_shape) conv_out = Conv1D(filters=64, kernel_size=3, activation='relu')(inputs) pool_out = MaxPooling1D(pool_size=2)(conv_out) flat_out = Flatten()(pool_out) return inputs, flat_out def create_lstm_model(input_shape): inputs = Input(shape=input_shape) lstm_out = LSTM(units=50, return_sequences=True)(inputs) return inputs, lstm_out def build_attention_layer(lstm_output): attention_probs = Dense(1, activation='softmax')(lstm_output) attention_mul = multiply([attention_probs, lstm_output]) return attention_mul input_data = ... # 加载数据集 scaler = MinMaxScaler(feature_range=(0, 1)) scaled_input = scaler.fit_transform(input_data) cnn_inputs, cnn_outputs = create_cnn_model((timesteps, features_num)) lstm_inputs, lstm_outputs = create_lstm_model((timesteps, features_num)) combined_features = concatenate([Flatten()(cnn_outputs), lstm_outputs]) attention_applied = build_attention_layer(combined_features) final_dense = Dense(output_dim)(attention_applied) model = Model(inputs=[cnn_inputs, lstm_inputs], outputs=final_dense) woa_instance = WhaleOptimizationAlgorithm() optimized_params = woa_instance.optimize(model.get_weights()) model.compile(optimizer='adam', loss='mean_squared_error') history = model.fit( [scaled_input, scaled_input], target, epochs=epochs, batch_size=batch_size, validation_split=validation_ratio ) ``` 上述代码片段展示了如何组合CNN、LSTM以及Attention层创建一个混合模型，并使用WOA来进行权重初始化或调优[^3]。