Refereed full papers (journals, book chapters, international conferences)

1997

@inproceedings{SSY1997,
	vgclass =	{refpap},
	vgproject =	{nn},
	author =	{Jun Shen and Huaijiang Sun and Jingyu Yang},
	title =	{Fuzzy Neural Nets with Asymmetric $\pi$ Membership
	Functions and Application to Texture Classification},
	booktitle =	{The 10th Scandinavian Conference on Image Analysis},
	address =	{Lappeenranta, Finland},
	pages =	{111--118},
	month =	{June},
	year =	{1997},
	abstract =	{Texture classification is in essential a problem of
	finding an optimal mapping from texture feature space to texture class
	space. In general, the a priori knowledge of textures in real problems
	is described in terms of expert rules and in terms of known samples.
	Statistical classification systems or classical fuzzy logic systems
	have difficulty to integrate both kinds of knowledge. In the present
	paper, we propose neural fuzzy systems (NFS) using asymmetric $\pi$
	membership functions (APF), their learning algorithm based on a new
	global optimization criterion and the application to texture
	classification. The NFS using APF shows the following advantages: (1)
	The APF gives a more general model of fuzzy rules, which improves the
	precision of NFS. (2) The smoothness of APF assures a good convergence
	of the system, which avoids oscillations in learning. (3) Based on the
	new global optimization criterion, the NFS can integrate both the
	expert knowledge in terms of fuzzy rules and the numerical training
	data for system learning, which is difficult for classical multilayer
	network or FLS. (4) The learning algorithm is simple, which is similar
	to that of classical multilayer network. (5) The NFS permits a
	refinement of the initial expert knowledge, and the new fuzzy rules
	found are easy to interpret. (6) When more training data are available
	in the future, a new training will demand less time of learning and
	realize a natural forgetting effect for non-precise initial expert
	knowledge and ancient data, which is desired for many intelligent
	systems. The NFS using APF is implemented and applied to texture
	classification, experimental comparison with other methods shows the
	good performance of such systems.},
}