人工神经网络是实现生物对应生物神经网络简化模型的计算范式。生物神经网络是神经元及其树突连接的局部组合，形成大脑。20世纪50年代末，大规模计算机的出现使得神经网络能够用于大脑样数计算，如模式识别、决策、机动控制和许多其他方法。传统计算机依赖于使用预先确定的一系列步骤（称为算法）解决问题的程序。这些程序由单个复杂的中央处理单元控制，并存储在内存中的特定位置的信息。人工神经网络使用高度分布式的表示和转换，并行运行，通过许多高度互联的神经元进行分布式控制，并将其信息存储在可变强度连接中，称为突触，就像人脑一样。要训练神经网络，您必须有一个包含与结果对应的样本参数的数据集。用于培训的数据通常使用已知结果的历史数据获取。您还可以通过创建示例问题和答案来训练神经网络。一旦训练过程完成，神经网络将能够预测新输入处理时的答案。
版本历史记录
- 版本 5.0 发布于 2000-01-01
提高速度和准确性
软件信息
- 软件分类： 发展 > 其他
- 发布者： RG Software Corporation
- 许可： 免费试用
- 价格： $240.00
- 版本： 5.0
- 适用平台： windows
终端用户许可协议
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