The concept of a recently proposed Forward-Forward learning algorithm for fully connected artificial neural networks is applied to a single multi output perceptron for classification. The parameters of the system are trained with respect to increased (decreased) "goodness" for correctly (incorrectly) labelled input samples. Basic numerical tests demonstrate that the trained perceptron effectively deals with data sets that have non-linear decision boundaries. Moreover, the overall performance is comparable to more complex neural networks with hidden layers. The benefit of the approach presented here is that it only involves a single matrix multiplication.

翻译：近期提出的全连接人工神经网络前向-前向学习算法概念被应用于单层多输出感知机分类任务。系统参数通过提升（降低）正确（错误）标记输入样本的"优良度"进行训练。基础数值实验表明，训练后的感知机能有效处理具有非线性决策边界的数据集。此外，其整体性能可与含隐藏层的更复杂神经网络相媲美。本方法的优势在于仅需单次矩阵乘法运算。