本文提出了基于粒子群（PSO）的訓(xùn)練ANN 的新算法，以此為基礎(chǔ)建立了對庫存品進(jìn)行ABC 分類的模型。新算法充分結(jié)合了PSO 與BP 兩者的優(yōu)勢，在訓(xùn)練過程中能同時優(yōu)化權(quán)值以及神經(jīng)元log-Sigmoid函數(shù)。實驗結(jié)果表明，新算法是企業(yè)庫存信息管理系統(tǒng)中進(jìn)行決策預(yù)測的一種可行方法。
關(guān)鍵字：粒子群（PSO）算法；ABC 分類；神經(jīng)網(wǎng)絡(luò)；反向傳播算法
Abstract: This paper presents a new ANN training algorithm based on PSO for ABC classification of stock keeping units (SKUs). The new algorithm combines the strengths of BP training algorithm and PSO-Gain training algorithm; it can both optimize the weights of network and the log-sigmoid function of the network during the training process. The experiment results show that the new algorithm is a viable way for decision-making in information management system of enterprise inventory.
Keywords: Particle swarm optimization; ABC classification; Neural network; Back propagation