普通智能家用电器所采用廉价“模糊控制”智能控制技术。少数高档家电用到”神经网络“技术（也叫神经网络模糊控制技术），模糊控制技术目前是智能家用电器使用最广泛的智能控制技术。原因在于这种技术和人的思维有一致性，理解较为方便且不需要高深的数学知识表达，可以用单片机进行构造。
Ordinary intelligent household appliances adopt cheap "fuzzy control" intelligent control technology. A few high-end household appliances use "neural network" technology (also known as neural network fuzzy control technology). At present, fuzzy control technology is the most widely used intelligent control technology for intelligent household appliances. The reason is that this technology is consistent with human thinking, easy to understand and does not need advanced mathematical knowledge expression. It can be constructed with single chip microcomputer.
不过模糊逻辑及其控制技术也存在一个不足的地方，即没有学习能力，从而使模糊控制家电产品难以积累经验。而知识的获取和经验的积累并由此所产生新的思维是人类智能的最明显体现。家用电器在运行过程中存在外部环境差异、内部零件损耗及用户使用习惯的问题，这就需要家用电器能对这些状态进行学习。例如一台洗衣机在春、夏、秋、冬四个季节外界环境是不一样的，由于水温及环境温度不同，洗涤时的程序也有区别，洗衣机应能自动学习不同环境中的洗涤程序；另外，在洗衣机早期应用中，洗衣机的零件处于紧耦合状态，过了磨合期，洗衣机的零件处于顺耦合状态，长期应用之后，洗衣机的零件处于松耦合状态。对于不同时期，洗衣机应该对自身状态进行恰当的调整，同时还应产生与之相应的优化控制过程；此外，洗衣机在很多次数的洗涤中，应自动学习特定衣质、衣量条件下的最优洗涤程序，当用户放入不同量、不同质的衣服时，洗衣机应自动进入学习后的最优洗涤程序——这就需要一种新的智能技术：神经网络控制。
However, there is also a deficiency in fuzzy logic and its control technology, that is, there is no learning ability, which makes it difficult for fuzzy control household appliances to accumulate experience. The acquisition of knowledge and the accumulation of experience and the resulting new thinking are the most obvious embodiment of human intelligence. There are differences in external environment, internal parts loss and users' use habits in the operation of household appliances For example, the external environment of a washing machine is different in spring, summer, autumn and winter. Due to different water temperature and ambient temperature, the washing procedures are also different. The washing machine should be able to automatically learn the washing procedures in different environments. In addition, in the early application of the washing machine, the parts of the washing machine are in a tight state Coupling state: after the running in period, the parts of the washing machine are in the forward coupling state. After long-term application, the parts of the washing machine are in the loose coupling state. For different periods, the washing machine should properly adjust its own state and produce the corresponding optimization control process; in addition, the washing machine should automatically learn specific clothing quality and quality in many times of washing The optimal washing program under the condition of quantity. When users put in different quantities and different quality clothes, the washing machine should automatically enter the optimal washing program after learning - which requires a new intelligent technology: neural network control.