【元胞自动机】基于元胞自动机模拟森林大火matlab源码.md

【元胞自动机】基于元胞自动机模拟双车道交通流模型含靠右行驶matlab源码.md

【元胞自动机】基于元胞自动机模拟商场人流matlab源码.md

【元胞自动机】基于元胞自动机实现多车道信号交叉口仿真matlab源码.md

【元胞自动机】基于元胞自动机实现图像处理matlab源码.md

三、运行结果.md

无人机通信网络资源分配与优化matlab源码.md

Adaptive Simulated Annealing (ASA) is a C-language code developed to statistically find the best global fit of a nonlinear constrained non-convex cost-function over a D-dimensional space. This algorithm permits an annealing schedule for "temperature" T decreasing exponentially in annealing-time k, T = T_0 exp(-c k^1/D). The introduction of re-annealing also permits adaptation to changing sensitivities in the multi-dimensional parameter-space. This annealing schedule is faster than fast Cauchy annealing, where T = T_0/k, and much faster than Boltzmann annealing, where T = T_0/ln k. ASA has over 100 OPTIONS to provide robust tuning over many classes of nonlinear stochastic systems.

text_classify 以复旦中文文本分类语料库为样本，用python实现的文本分类算法 由于训练集和测试集太大，所以给出的是训练集和测试集的下载地址，以及停用词表hlt_stop_words.txt Python版本为2.7 运行顺序： step1：corpus_segment.py step2: corpus2Bunch.py (需要提前创建目录train_word_bag和test_word_bag) step3: TFIDF_space.py step4:NBayes_Predict.py

bayesian-curve-fitting:基于贝叶斯推理的曲线拟合算法