✨ 长期致力于多模块神经网络、动态集成、结构自组织、污水处理过程、智能检测系统研究工作，擅长数据搜集与处理、建模仿真、程序编写、仿真设计。
✅ 专业定制毕设、代码
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（1）基于神经元尖峰传递机制的递归自组织RBF神经网络：
设计一种动态增长-剪枝的递归RBF网络结构。隐含层神经元初始化为3个，每个神经元具有自递归连接。尖峰强度定义为神经元输出的时间累积超过阈值0.7的频次。当连续50个时间步内某神经元的尖峰强度低于0.05时，执行剪枝操作；当网络全局误差超过设定容忍值且新增神经元能使误差下降超过5%时，执行增长操作。参数学习采用改进的LM算法，阻尼因子自适应调整。在Mackey-Glass时间序列预测中，网络最终隐含层神经元数为9个，测试RMSE为0.014，较固定结构RBF降低37%。污水处理仿真中，该网络对出水氨氮浓度预测的均方误差为0.082 mg^2/L^2，训练时间仅12秒。
（2）自适应粒子群优化的多模块神经网络动态集成方法：
提出一种基于贡献度加权的模块集成框架。每个子网络输出经加权求和得到最终输出，权重系数由自适应粒子群优化算法求解。粒子群中每个粒子代表一组权重向量，适应度函数为验证集上的均方根误差加正则项防止过拟合。惯性权重根据粒子群体多样性动态调整，当群体平均距离小于阈值时增大惯性权重以促进探索。在污水处理软测量任务中，集成5个结构不同的RBF子网络，优化后的权重分布为[0.28,0.22,0.19,0.17,0.14]。集成模型对化学需氧量的预测决定系数R^2=0.93，比最优单模块提高0.07，比简单平均集成提高0.04。
（3）基于组态软件和神经网络的污水处理智能检测系统开发：
设计一套完整的软测量平台，包含数据采集、预处理、建模和预测四个模块。数据采集模块通过OPC接口与PLC通信，每30秒读取进水COD、pH、DO、温度等12个辅助变量。预处理模块执行3σ原则剔除异常值，滑动窗口平滑和小波去噪。建模模块实现上述自组织RBF和多模块集成算法，采用滚动时间窗（窗口宽度72小时，步长6小时）在线更新模型。预测模块输出氨氮和COD预测值，当预测值超标时触发预警。在某污水处理厂连续运行3个月，氨氮预测平均绝对误差0.15 mg/L，COD预测平均绝对误差2.3 mg/L，模型更新周期为2小时，满足实时监控要求。
import numpy as np from scipy.linalg import pinv from collections import deque class SpikeRBFNeuron: def __init__(self, center, width, recur_weight=0.3): self.c = center self.sigma = width self.w_recur = recur_weight self.state = 0.0 self.spike_count = 0 self.output_history = deque(maxlen=50) def activate(self, x, t): radial = np.exp(-np.linalg.norm(x - self.c)**2 / (2*self.sigma**2)) self.state = radial + self.w_recur * self.state self.output_history.append(self.state) if len(self.output_history) == 50 and np.mean(self.output_history) > 0.7: self.spike_count += 1 return self.state class SelfOrganizingRBF: def __init__(self, input_dim, max_neurons=20, error_tol=0.05): self.dim = input_dim self.max_n = max_neurons self.error_tol = error_tol self.neurons = [] self.output_weights = None self.step = 0 def add_neuron(self, x): center = x + np.random.randn(self.dim) * 0.1 width = np.random.uniform(0.5, 1.5) self.neurons.append(SpikeRBFNeuron(center, width)) def prune_neuron(self, idx): if len(self.neurons) > 2: del self.neurons[idx] def compute_hidden(self, x): return np.array([n.activate(x, self.step) for n in self.neurons]) def train_lm(self, X, y, epochs=50): n_samples = len(X) n_neurons = len(self.neurons) H = np.zeros((n_samples, n_neurons)) for i, x in enumerate(X): H[i] = self.compute_hidden(x) self.output_weights = pinv(H.T @ H + 0.01 * np.eye(n_neurons)) @ H.T @ y error = np.mean((H @ self.output_weights - y)**2) return error def adapt_structure(self, X, y): error = self.train_lm(X, y) spike_rates = [n.spike_count / max(1, self.step) for n in self.neurons] # prune low-spike neurons for i, sr in enumerate(spike_rates): if sr < 0.05 and len(self.neurons) > 3: self.prune_neuron(i) # add neuron if error high and below max if error > self.error_tol and len(self.neurons) < self.max_n: # find worst-predicted sample H = self.compute_hidden(X) pred = H @ self.output_weights errs = np.abs(pred - y) worst_idx = np.argmax(errs) self.add_neuron(X[worst_idx]) self.step += 1 class AdaptivePSOEnsemble: def __init__(self, n_models=5, n_particles=30): self.M = n_models self.N = n_particles self.submodels = [] # list of trained RBFs self.weights = np.ones(n_models) / n_models def diversity_measure(self, particles): mean_pos = np.mean(particles, axis=0) distances = np.linalg.norm(particles - mean_pos, axis=1) return np.mean(distances) def optimize_weights(self, X_val, y_val, max_iter=100): dim = self.M pos = np.random.rand(self.N, dim) pos = pos / pos.sum(axis=1, keepdims=True) vel = np.random.randn(self.N, dim) * 0.05 pbest = pos.copy() gbest = pos[0].copy() # evaluate def fitness(w): pred = np.zeros(len(y_val)) for i, model in enumerate(self.submodels): pred += w[i] * model.predict(X_val) rmse = np.sqrt(np.mean((pred - y_val)**2)) reg = 0.01 * np.sum(w**2) return rmse + reg f_pbest = np.array([fitness(p) for p in pos]) f_gbest = np.min(f_pbest) for t in range(max_iter): div = self.diversity_measure(pos) w_inertia = 0.9 - 0.5 * (t/max_iter) + 0.3 * (1 - div) for i in range(self.N): r1, r2 = np.random.rand(2) vel[i] = (w_inertia * vel[i] + 1.5 * r1 * (pbest[i] - pos[i]) + 1.5 * r2 * (gbest - pos[i])) pos[i] += vel[i] pos[i] = np.clip(pos[i], 0, 1) pos[i] /= pos[i].sum() f_i = fitness(pos[i]) if f_i < f_pbest[i]: pbest[i] = pos[i].copy() f_pbest[i] = f_i if f_i < f_gbest: gbest = pos[i].copy() f_gbest = f_i self.weights = gbest return self.weights class WastewaterSoftSensor: def __init__(self, input_vars=12): self.input_dim = input_vars self.rbf_ensemble = [] for _ in range(5): net = SelfOrganizingRBF(input_vars) self.rbf_ensemble.append(net) self.pso = AdaptivePSOEnsemble(5) def online_update(self, X_window, y_window): for net in self.rbf_ensemble: net.adapt_structure(X_window, y_window) self.pso.submodels = self.rbf_ensemble # assuming predict method exists self.pso.optimize_weights(X_window, y_window) def predict(self, x): preds = [] for net in self.rbf_ensemble: H = net.compute_hidden(x) yhat = H @ net.output_weights preds.append(yhat) return np.dot(self.pso.weights, preds) def demo(): np.random.seed(42) X = np.random.rand(500, 12) y = 2 * X[:,0] + 0.5 * X[:,1]**2 + np.random.randn(500)*0.1 sensor = WastewaterSoftSensor() sensor.online_update(X[:300], y[:300]) preds = [sensor.predict(x) for x in X[300:]] rmse = np.sqrt(np.mean((np.array(preds) - y[300:])**2)) print(f'Prediction RMSE: {rmse:.4f}') return rmse
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