Abstract

We present an adaptive neuro-fuzzy inference system (ANFIS) strategy to generate dot patterns in a liquid-crystal display light guide panel. The ANFIS model combines the learning capabilities of neural networks and the knowledge illustration of fuzzy logic systems using linguistic expressions. A hybrid learning algorithm, based on the least square method and the back propagation algorithm, is utilized to identify the parameters of ANFIS. Two inputs of ANFIS are the dot radius and the distance from dots to a light source, and one output is the illuminance over a light guide panel. During the process of generating diffuser dot patterns, ANFIS carries out efficient input selection, rule creation, networks training, and parameter estimation to create an appropriate model by the learning algorithm. The results show that the proposed model can achieve an even illuminance condition and effectively improve brightness in accordance with the light source position. Moreover, a comparative analysis suggests that the ANFIS-based approach outperforms the traditional model in terms of overall illuminance and color uniformity.

© 2010 Optical Society of America

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  1. A. Tagaya, M. Nagai, Y. Koike, and K. Yokoyama, “Thin liquid-crystal display backlight system with highly scattering optical transmission polymers,” Appl. Opt. 40, 6274–6280 (2001).
    [CrossRef]
  2. X. Yang, Y. Yan, and G. Jin, “High-efficiency integrated polarized backlight system for liquid crystal display,” Appl. Phys. Lett. 88, 221109 (2006).
    [CrossRef]
  3. F. Di, G. Jin, Y. Yan, and S. Fan, “High quality light guide plates that can control the illumination angle based on microprism structures,” Appl. Phys. Lett. 85, 6016–6018 (2004).
    [CrossRef]
  4. G. Lee, J. H. Jeong, S. J. Yoon, and D. H. Choi, “Design optimization for optical patterns in a light-guide panel to improve illuminance and uniformity of the liquid-crystal display,” Opt. Eng. 48, 024001 (2009).
    [CrossRef]
  5. J. G. Chang and C. T. Lee, “Random-dot pattern design of a light guide in an edge-lit backlight: integration of optical design and dot generation scheme by the molecular-dynamics method,” J. Opt. Soc. Am. A 24, 839–849 (2007).
    [CrossRef]
  6. J. G. Chang, M. H. Su, C. T. Lee, and C. C. Hwang, “Generating random and nonoverlapping dot patterns for liquid-crystal display backlight light guides using molecular-dynamics method,” J. Appl. Phys. 98, 114910 (2005).
    [CrossRef]
  7. T. Idé, H. Mizuta, H. Numata, Y. Taira, M. Suzuki, M. Noguchi, and Y. Katsu, “Dot pattern generation technique using molecular dynamics,” J. Opt. Soc. Am. A 20, 248–255 (2003).
    [CrossRef]
  8. W. Y. Lee, T. K. Lim, Y. W. Lee, and I. W. Lee, “Fast ray-tracing methods for LCD backlight simulation using the characteristics of the pattern,” Opt. Eng. 44, 014004 (2005).
    [CrossRef]
  9. J. G. Chang and Y. B. Fang, “Dot-pattern design of a light guide in an edge-lit backlight using a regional partition approach,” Opt. Eng. 46, 043002 (2007).
    [CrossRef]
  10. R. Fullér, Introduction to Neuro-Fuzzy Systems (Springer-Verlag, 2000).
  11. L. H. Tsoukalas and R. E. Uhrig, Fuzzy and Neural Approaches in Engineering (Wiley, 1997).
  12. R. R. Yager and L. A. Zadeh, Fuzzy Sets, Neural Networks, and Soft Computing (Van Nostrand Reinhold, 1994).
  13. J. S. R. Jang, “Anfis: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993).
    [CrossRef]
  14. M. Sugeno and T. Takagi, “Multidimensional fuzzy-reasoning,” Fuzzy Sets Syst. 9, 313–325 (1983).
    [CrossRef]
  15. R. J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, 1997).
  16. M. A. Shoorehdeli, M. Teshnehlab, A. K. Sedigh, and M. A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods,” Appl. Soft Comput. 9, 833–850 (2009).
    [CrossRef]
  17. Z. L. Sun, K. F. Au, and T. M. Choi, “A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines,” IEEE Trans. Syst. Man Cybern. 37, 1321–1331 (2007).
    [CrossRef]
  18. TracePro software for opto-mechanical modeling user’s manual, release 3.0 (Lambda Research Corporation, 2002).
  19. TracePro software for opto-mechanical modeling macro reference, release 3.2 (Lambda Research Corporation, 2004).

2009 (2)

M. A. Shoorehdeli, M. Teshnehlab, A. K. Sedigh, and M. A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods,” Appl. Soft Comput. 9, 833–850 (2009).
[CrossRef]

G. Lee, J. H. Jeong, S. J. Yoon, and D. H. Choi, “Design optimization for optical patterns in a light-guide panel to improve illuminance and uniformity of the liquid-crystal display,” Opt. Eng. 48, 024001 (2009).
[CrossRef]

2007 (3)

J. G. Chang and C. T. Lee, “Random-dot pattern design of a light guide in an edge-lit backlight: integration of optical design and dot generation scheme by the molecular-dynamics method,” J. Opt. Soc. Am. A 24, 839–849 (2007).
[CrossRef]

Z. L. Sun, K. F. Au, and T. M. Choi, “A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines,” IEEE Trans. Syst. Man Cybern. 37, 1321–1331 (2007).
[CrossRef]

J. G. Chang and Y. B. Fang, “Dot-pattern design of a light guide in an edge-lit backlight using a regional partition approach,” Opt. Eng. 46, 043002 (2007).
[CrossRef]

2006 (1)

X. Yang, Y. Yan, and G. Jin, “High-efficiency integrated polarized backlight system for liquid crystal display,” Appl. Phys. Lett. 88, 221109 (2006).
[CrossRef]

2005 (2)

J. G. Chang, M. H. Su, C. T. Lee, and C. C. Hwang, “Generating random and nonoverlapping dot patterns for liquid-crystal display backlight light guides using molecular-dynamics method,” J. Appl. Phys. 98, 114910 (2005).
[CrossRef]

W. Y. Lee, T. K. Lim, Y. W. Lee, and I. W. Lee, “Fast ray-tracing methods for LCD backlight simulation using the characteristics of the pattern,” Opt. Eng. 44, 014004 (2005).
[CrossRef]

2004 (2)

F. Di, G. Jin, Y. Yan, and S. Fan, “High quality light guide plates that can control the illumination angle based on microprism structures,” Appl. Phys. Lett. 85, 6016–6018 (2004).
[CrossRef]

TracePro software for opto-mechanical modeling macro reference, release 3.2 (Lambda Research Corporation, 2004).

2003 (1)

2002 (1)

TracePro software for opto-mechanical modeling user’s manual, release 3.0 (Lambda Research Corporation, 2002).

2001 (1)

2000 (1)

R. Fullér, Introduction to Neuro-Fuzzy Systems (Springer-Verlag, 2000).

1997 (2)

L. H. Tsoukalas and R. E. Uhrig, Fuzzy and Neural Approaches in Engineering (Wiley, 1997).

R. J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, 1997).

1994 (1)

R. R. Yager and L. A. Zadeh, Fuzzy Sets, Neural Networks, and Soft Computing (Van Nostrand Reinhold, 1994).

1993 (1)

J. S. R. Jang, “Anfis: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993).
[CrossRef]

1983 (1)

M. Sugeno and T. Takagi, “Multidimensional fuzzy-reasoning,” Fuzzy Sets Syst. 9, 313–325 (1983).
[CrossRef]

Au, K. F.

Z. L. Sun, K. F. Au, and T. M. Choi, “A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines,” IEEE Trans. Syst. Man Cybern. 37, 1321–1331 (2007).
[CrossRef]

Chang, J. G.

J. G. Chang and Y. B. Fang, “Dot-pattern design of a light guide in an edge-lit backlight using a regional partition approach,” Opt. Eng. 46, 043002 (2007).
[CrossRef]

J. G. Chang and C. T. Lee, “Random-dot pattern design of a light guide in an edge-lit backlight: integration of optical design and dot generation scheme by the molecular-dynamics method,” J. Opt. Soc. Am. A 24, 839–849 (2007).
[CrossRef]

J. G. Chang, M. H. Su, C. T. Lee, and C. C. Hwang, “Generating random and nonoverlapping dot patterns for liquid-crystal display backlight light guides using molecular-dynamics method,” J. Appl. Phys. 98, 114910 (2005).
[CrossRef]

Choi, D. H.

G. Lee, J. H. Jeong, S. J. Yoon, and D. H. Choi, “Design optimization for optical patterns in a light-guide panel to improve illuminance and uniformity of the liquid-crystal display,” Opt. Eng. 48, 024001 (2009).
[CrossRef]

Choi, T. M.

Z. L. Sun, K. F. Au, and T. M. Choi, “A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines,” IEEE Trans. Syst. Man Cybern. 37, 1321–1331 (2007).
[CrossRef]

Di, F.

F. Di, G. Jin, Y. Yan, and S. Fan, “High quality light guide plates that can control the illumination angle based on microprism structures,” Appl. Phys. Lett. 85, 6016–6018 (2004).
[CrossRef]

Fan, S.

F. Di, G. Jin, Y. Yan, and S. Fan, “High quality light guide plates that can control the illumination angle based on microprism structures,” Appl. Phys. Lett. 85, 6016–6018 (2004).
[CrossRef]

Fang, Y. B.

J. G. Chang and Y. B. Fang, “Dot-pattern design of a light guide in an edge-lit backlight using a regional partition approach,” Opt. Eng. 46, 043002 (2007).
[CrossRef]

Fullér, R.

R. Fullér, Introduction to Neuro-Fuzzy Systems (Springer-Verlag, 2000).

Hwang, C. C.

J. G. Chang, M. H. Su, C. T. Lee, and C. C. Hwang, “Generating random and nonoverlapping dot patterns for liquid-crystal display backlight light guides using molecular-dynamics method,” J. Appl. Phys. 98, 114910 (2005).
[CrossRef]

Idé, T.

Jang, J. S. R.

J. S. R. Jang, “Anfis: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993).
[CrossRef]

Jang, R. J. S.

R. J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, 1997).

Jeong, J. H.

G. Lee, J. H. Jeong, S. J. Yoon, and D. H. Choi, “Design optimization for optical patterns in a light-guide panel to improve illuminance and uniformity of the liquid-crystal display,” Opt. Eng. 48, 024001 (2009).
[CrossRef]

Jin, G.

X. Yang, Y. Yan, and G. Jin, “High-efficiency integrated polarized backlight system for liquid crystal display,” Appl. Phys. Lett. 88, 221109 (2006).
[CrossRef]

F. Di, G. Jin, Y. Yan, and S. Fan, “High quality light guide plates that can control the illumination angle based on microprism structures,” Appl. Phys. Lett. 85, 6016–6018 (2004).
[CrossRef]

Katsu, Y.

Khanesar, M. A.

M. A. Shoorehdeli, M. Teshnehlab, A. K. Sedigh, and M. A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods,” Appl. Soft Comput. 9, 833–850 (2009).
[CrossRef]

Koike, Y.

Lee, C. T.

J. G. Chang and C. T. Lee, “Random-dot pattern design of a light guide in an edge-lit backlight: integration of optical design and dot generation scheme by the molecular-dynamics method,” J. Opt. Soc. Am. A 24, 839–849 (2007).
[CrossRef]

J. G. Chang, M. H. Su, C. T. Lee, and C. C. Hwang, “Generating random and nonoverlapping dot patterns for liquid-crystal display backlight light guides using molecular-dynamics method,” J. Appl. Phys. 98, 114910 (2005).
[CrossRef]

Lee, G.

G. Lee, J. H. Jeong, S. J. Yoon, and D. H. Choi, “Design optimization for optical patterns in a light-guide panel to improve illuminance and uniformity of the liquid-crystal display,” Opt. Eng. 48, 024001 (2009).
[CrossRef]

Lee, I. W.

W. Y. Lee, T. K. Lim, Y. W. Lee, and I. W. Lee, “Fast ray-tracing methods for LCD backlight simulation using the characteristics of the pattern,” Opt. Eng. 44, 014004 (2005).
[CrossRef]

Lee, W. Y.

W. Y. Lee, T. K. Lim, Y. W. Lee, and I. W. Lee, “Fast ray-tracing methods for LCD backlight simulation using the characteristics of the pattern,” Opt. Eng. 44, 014004 (2005).
[CrossRef]

Lee, Y. W.

W. Y. Lee, T. K. Lim, Y. W. Lee, and I. W. Lee, “Fast ray-tracing methods for LCD backlight simulation using the characteristics of the pattern,” Opt. Eng. 44, 014004 (2005).
[CrossRef]

Lim, T. K.

W. Y. Lee, T. K. Lim, Y. W. Lee, and I. W. Lee, “Fast ray-tracing methods for LCD backlight simulation using the characteristics of the pattern,” Opt. Eng. 44, 014004 (2005).
[CrossRef]

Mizuta, H.

Mizutani, E.

R. J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, 1997).

Nagai, M.

Noguchi, M.

Numata, H.

Sedigh, A. K.

M. A. Shoorehdeli, M. Teshnehlab, A. K. Sedigh, and M. A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods,” Appl. Soft Comput. 9, 833–850 (2009).
[CrossRef]

Shoorehdeli, M. A.

M. A. Shoorehdeli, M. Teshnehlab, A. K. Sedigh, and M. A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods,” Appl. Soft Comput. 9, 833–850 (2009).
[CrossRef]

Su, M. H.

J. G. Chang, M. H. Su, C. T. Lee, and C. C. Hwang, “Generating random and nonoverlapping dot patterns for liquid-crystal display backlight light guides using molecular-dynamics method,” J. Appl. Phys. 98, 114910 (2005).
[CrossRef]

Sugeno, M.

M. Sugeno and T. Takagi, “Multidimensional fuzzy-reasoning,” Fuzzy Sets Syst. 9, 313–325 (1983).
[CrossRef]

Sun, C. T.

R. J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, 1997).

Sun, Z. L.

Z. L. Sun, K. F. Au, and T. M. Choi, “A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines,” IEEE Trans. Syst. Man Cybern. 37, 1321–1331 (2007).
[CrossRef]

Suzuki, M.

Tagaya, A.

Taira, Y.

Takagi, T.

M. Sugeno and T. Takagi, “Multidimensional fuzzy-reasoning,” Fuzzy Sets Syst. 9, 313–325 (1983).
[CrossRef]

Teshnehlab, M.

M. A. Shoorehdeli, M. Teshnehlab, A. K. Sedigh, and M. A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods,” Appl. Soft Comput. 9, 833–850 (2009).
[CrossRef]

Tsoukalas, L. H.

L. H. Tsoukalas and R. E. Uhrig, Fuzzy and Neural Approaches in Engineering (Wiley, 1997).

Uhrig, R. E.

L. H. Tsoukalas and R. E. Uhrig, Fuzzy and Neural Approaches in Engineering (Wiley, 1997).

Yager, R. R.

R. R. Yager and L. A. Zadeh, Fuzzy Sets, Neural Networks, and Soft Computing (Van Nostrand Reinhold, 1994).

Yan, Y.

X. Yang, Y. Yan, and G. Jin, “High-efficiency integrated polarized backlight system for liquid crystal display,” Appl. Phys. Lett. 88, 221109 (2006).
[CrossRef]

F. Di, G. Jin, Y. Yan, and S. Fan, “High quality light guide plates that can control the illumination angle based on microprism structures,” Appl. Phys. Lett. 85, 6016–6018 (2004).
[CrossRef]

Yang, X.

X. Yang, Y. Yan, and G. Jin, “High-efficiency integrated polarized backlight system for liquid crystal display,” Appl. Phys. Lett. 88, 221109 (2006).
[CrossRef]

Yokoyama, K.

Yoon, S. J.

G. Lee, J. H. Jeong, S. J. Yoon, and D. H. Choi, “Design optimization for optical patterns in a light-guide panel to improve illuminance and uniformity of the liquid-crystal display,” Opt. Eng. 48, 024001 (2009).
[CrossRef]

Zadeh, L. A.

R. R. Yager and L. A. Zadeh, Fuzzy Sets, Neural Networks, and Soft Computing (Van Nostrand Reinhold, 1994).

Appl. Opt. (1)

Appl. Phys. Lett. (2)

X. Yang, Y. Yan, and G. Jin, “High-efficiency integrated polarized backlight system for liquid crystal display,” Appl. Phys. Lett. 88, 221109 (2006).
[CrossRef]

F. Di, G. Jin, Y. Yan, and S. Fan, “High quality light guide plates that can control the illumination angle based on microprism structures,” Appl. Phys. Lett. 85, 6016–6018 (2004).
[CrossRef]

Appl. Soft Comput. (1)

M. A. Shoorehdeli, M. Teshnehlab, A. K. Sedigh, and M. A. Khanesar, “Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods,” Appl. Soft Comput. 9, 833–850 (2009).
[CrossRef]

Fuzzy Sets Syst. (1)

M. Sugeno and T. Takagi, “Multidimensional fuzzy-reasoning,” Fuzzy Sets Syst. 9, 313–325 (1983).
[CrossRef]

IEEE Trans. Syst. Man Cybern. (2)

J. S. R. Jang, “Anfis: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993).
[CrossRef]

Z. L. Sun, K. F. Au, and T. M. Choi, “A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines,” IEEE Trans. Syst. Man Cybern. 37, 1321–1331 (2007).
[CrossRef]

J. Appl. Phys. (1)

J. G. Chang, M. H. Su, C. T. Lee, and C. C. Hwang, “Generating random and nonoverlapping dot patterns for liquid-crystal display backlight light guides using molecular-dynamics method,” J. Appl. Phys. 98, 114910 (2005).
[CrossRef]

J. Opt. Soc. Am. A (2)

Opt. Eng. (3)

G. Lee, J. H. Jeong, S. J. Yoon, and D. H. Choi, “Design optimization for optical patterns in a light-guide panel to improve illuminance and uniformity of the liquid-crystal display,” Opt. Eng. 48, 024001 (2009).
[CrossRef]

W. Y. Lee, T. K. Lim, Y. W. Lee, and I. W. Lee, “Fast ray-tracing methods for LCD backlight simulation using the characteristics of the pattern,” Opt. Eng. 44, 014004 (2005).
[CrossRef]

J. G. Chang and Y. B. Fang, “Dot-pattern design of a light guide in an edge-lit backlight using a regional partition approach,” Opt. Eng. 46, 043002 (2007).
[CrossRef]

Other (6)

R. Fullér, Introduction to Neuro-Fuzzy Systems (Springer-Verlag, 2000).

L. H. Tsoukalas and R. E. Uhrig, Fuzzy and Neural Approaches in Engineering (Wiley, 1997).

R. R. Yager and L. A. Zadeh, Fuzzy Sets, Neural Networks, and Soft Computing (Van Nostrand Reinhold, 1994).

TracePro software for opto-mechanical modeling user’s manual, release 3.0 (Lambda Research Corporation, 2002).

TracePro software for opto-mechanical modeling macro reference, release 3.2 (Lambda Research Corporation, 2004).

R. J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Prentice Hall, 1997).

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Figures (10)

Fig. 1
Fig. 1

Basic structure of LCD backlight.

Fig. 2
Fig. 2

Simplified dot patterns in a light guide. The dot radii vary with distance from the light source.

Fig. 3
Fig. 3

(a) Fuzzy reasoning mechanism. (b) Equivalent ANFIS architecture.

Fig. 4
Fig. 4

Dot-pattern generation system for ANFIS structure.

Fig. 5
Fig. 5

Adaptation of parameter steps of ANFIS.

Fig. 6
Fig. 6

Curve of network error convergence of ANFIS.

Fig. 7
Fig. 7

Flow chart for the proposed ANFIS model.

Fig. 8
Fig. 8

(a) Initial and (c) final Gaussian MF of input 1 (dot radius) and (b) initial and (d) final triangular MF of input 2 (distance).

Fig. 9
Fig. 9

Comparison of dot radius distribution using the conventional and ANFIS methods.

Fig. 10
Fig. 10

Illuminance distribution on target surface. On each map, the left is the whole illuminance distribution and the right is the illuminance distribution of each point along the horizontal and vertical lines that cross the center point (0, 0): (a) by the conventional method with 5,000,000 traced rays, (b) by the ANFIS method with 5,000,000 traced rays, (c) Gaussian smoothing to Fig. 10a, and (d) Gaussian smoothing to Fig. 10b.

Equations (12)

Equations on this page are rendered with MathJax. Learn more.

D = A dot A = i k π r i 2 L · W Φ out Φ in ,
E ave = 1 k i k E i .
O i l = μ A i ( x ) , i = 1 , 2 or O i l = μ B i 2 ( y ) , i = 3 , 4 ,
triangle ( x ; a , b , c ) = { 0 , x a x a / b a , a x b c x / c b , b x c 0 , c x ,
gaussian ( x ; c , σ ) = exp [ ( ( x c ) / σ ) 2 ] ,
O i 2 = w i = μ A i ( x ) × μ B i ( y ) = O 1 i × O 2 i , i = 1 , 2.
O i 3 = w ¯ i = w i w 1 + w 2 , i = 1 , 2.
O i 4 = w ¯ i f i = w ¯ i ( p i x + q i y + r i ) , i = 1 , 2 ,
O i 5 = i w ¯ i f i = i w ¯ i f i i w ¯ i , i = 1 , 2.
f = w ¯ 1 f 1 + w ¯ 2 f 2 = ( w ¯ 1 x ) p 1 + ( w ¯ 1 y ) q 1 + ( w ¯ 1 ) r 1 + ( w ¯ 2 x ) p 2 + ( w ¯ 2 y ) q 2 + ( w ¯ 2 ) r 2 .
Δ α = η E α ,
η = k α ( E / α ) 2 ,

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